• Journal of the Institute of Electronics Engineers of Korea SP
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• v.44 no.2 s.314
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• pp.46-54
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• 2007

In current printer industry, four or more colorants are added for color gamut extension because the gamut of printer is smaller than other devices. However, these additional colorants make a redundancy problem that several combinations of colorants reproduced same color stimulus in colorimetric inverse characterization process. Thus, we propose a method of colorimetric inverse characterization using color correlation between colorant's amount. First, for analyzing the combination of colorants which represent the same color stimulus, we estimate the color stimulus for all combination of colorants by Cellular Yule-Nielsen Spectral Neugebauer printer model. The combination of colorants which has higher color correlation factor comparing combinations of colorant around itself in color space is selected. It can reduced the color difference from the tetrahedral interpolation process which is estimation of the output value(colorants combination) for arbitrary input(color stimulus). The selected combinations of colorants and their color stimulus are stored to the lookup table. In experiment, the CMYKGO printer was used. As a result, the dark region of color gamut was extended and the color tone was more naturally represented.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.12
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• pp.746-755
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• 2014

Compressive sensing (CS) has drawn much interest as a new sampling technique that enables signals to be sampled at a much lower than the Nyquist rate. By noting that the block-based compressive sensing can still keep spatial correlation in measurement domain, in this paper, we propose a novel encoding technique for measurement data obtained in the block-based CS of natural image. We apply discrete wavelet transform (DWT) to decorrelate CS measurements and then assign a proper quantization scheme to those DWT coefficients. Thus, redundancy of CS measurements and bitrate of system are reduced remarkably. Experimental results show improvements in rate-distortion performance by the proposed method against two existing methods of scalar quantization (SQ) and differential pulse-code modulation (DPCM). In the best case, the proposed method gains up to 4 dB, 0.9 dB, and 2.5 dB compared with the Block-based CS-Smoothed Projected Landweber plus SQ, Block-based CS-Smoothed Projected Landweber plus DPCM, and Multihypothesis Block-based CS-Smoothed Projected Landweber plus DPCM, respectively.

• Horticultural Science & Technology
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• v.30 no.6
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• pp.734-742
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• 2012

To compare RNA interference-mediated gene silencing technique and T-DNA integration for gene function analysis in Chinese cabbage, BrSAMS-knockout (KO) line and BrSAMS-knockdown (KD) line were used. The KO line had lost the function of a Brassica rapa S-adenosylmethionine synthetase (BrSAMS) gene by T-DNA insertion and the KD line had shown down-regulated BrSAMS genes' expression by dsRNA cleavage. From microarray results of the KO and KD lines, genes linked to SAMS such as sterol, sucrose, homogalacturonan biosynthesis and glutaredoxin-related protein, serine/threonine protein kinase, and gibberellin-responsive protein showed distinct differences in their expression levels. Even though one BrSAMS gene in the KO line was broken by T-DNA insertion, gene expression pattern of that line did not show remarkable differences compared to wild type control. However, the KD line obtained by RNAi technique showed prominent difference in its gene expression. Besides, change of polyamine and ethylene synthesis genes directly associated with BrSAMS was displayed much more in the KD line. In the microarray analysis of the KO line, BrSAMS function could not be clearly defined because of BrSAMS redundancy due to the genome triplication events in Brassicaceae. In conclusion, we supposed that gene knock-down method by RNAi silencing is more effective than knock-out method by T-DNA insertion for gene function analysis of polyploidy crops such as Chinese cabbage.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.9
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• pp.1070-1074
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• 2014

According to IEEE 1662(2009), IPS is a power system where all prime movers produce electrical power that is shared among propulsion, mission, and ship service loads. Discriminating attributes of integrated power systems are flexibility of movers' arrangements, mechanical decoupling between prime movers and propulsors, an increased level of energy conversion and transmission redundancy, and flexibility of redistributing available electrical power for future electronic weapons. IPS could have various steps of power that can be produced at optimal load of movers. In this study, an evaluation method for optimal arrangement of movers was investigated when an IPS warship is projected. The two factors are utilized for the quantitative analysis which are the weight of system as the fighting power and the fuel consumption per year as the economic feasibility. And also the ways for arrangement of system were studied according to existence of small diesel generator. The evaluation method that decides the optimization level is based on the DEA(Data Envelopment Analysis)

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.5
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• pp.105-119
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• 2020

Although there have been a lot of efforts to improve water quality in the estuarine reservoir, overall the water quality problems of the estuarine reservoirs remain. So, it is essential to establish water quality management plans under a comprehensive understanding of the environmental characteristics of the estuarine reservoir. Therefore, in this study, a resilience analysis framework for evaluating the estuarine reservoir's water quality was suggested for improving existing assessment method for water quality management plan. First, as a result of analyzing the static resilience to each scenario, it was found that from the S3 scenario in which dredging was conducted considerably, the resilience of about 30% more than the current estuarine reservoir system was restored. Second, as a result of analyzing the dynamic resilience, if cost and time are considered, there is no significant difference in robustness and resourcefulness, so it can be seen that the resilience of the estuarine reservoir can be efficiently improved by simply performing dredging up to the level of Scenario 3. Finally, as a result of comparing static and dynamic resilience, since static resilience is only presented as a single value, the differences and characteristics of the resilience capacity of the estuarine reservoir might be overlooked only by the static resilience analysis. However, in the aspect that it is possible to interpret the internal recovery capacity of the estuarine reservoir in multiple ways with various indicators (robustness, redundancy, resourcefulness, rapidity), evaluating water quality based on dynamic resilience analysis is useful.

• The Journal of the Acoustical Society of Korea
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• v.21 no.5
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• pp.430-437
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• 2002

Having a variety of good characteristics against other pattern recognition techniques, the multilayer perceptron (MLP) has been widely used in speech recognition and speaker recognition. But, it is known that the error backpropagation (EBP) algorithm that MLP uses in learning has the defect that requires restricts long learning time, and it restricts severely the applications like speaker recognition and speaker adaptation requiring real time processing. Because the learning data for pattern recognition contain high redundancy, in order to increase the learning speed it is very effective to use the online-based learning methods, which update the weight vector of the MLP by the pattern. A typical online EBP algorithm applies the fixed learning rate for each update of the weight vector. Though a large amount of speedup with the online EBP can be obtained by choosing the appropriate fixed rate, firing the rate leads to the problem that the algorithm cannot respond effectively to different learning phases as the phases change and the number of patterns contributing to learning decreases. To solve this problem, this paper proposes a Changing rate and Omitting patterns in Instant Learning (COIL) method to apply the variable rate and the only patterns necessary to the learning phase when the phases come to change. In this paper, experimentations are conducted for speaker verification and speech recognition, and results are presented to verify the performance of the COIL.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.5
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• pp.267-274
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• 2018

For successful BIM settlement, it is a key technic for engineer to design structures in the 3-dimensional digital space and to work out related design documents directly. Lately many BIM tool has been released and each supports their 3-dimensional object libraries. But it is not easy to apply those libraries to design transportation infra structures that were placed along the route(3-dimensional line). Moreover, in case of design changes, it is so difficult to reflect those changes with the integrated model that was assembled by them. Because of they were developed without consideration for redundancy of parameters between objects that were placed nearby or were related each other. In this paper, a method to develop module for modeling and placing 3-dimensional object for transportation infra structures is presented. The modules are employed by a parametric method and can deal with design changes. Also, for a railroad bridge, through developing user interface of the integrated 3-dimensional model that was assembled by those modules the applicability of them was reviewed.

• Journal of KIISE:Databases
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• v.30 no.4
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• pp.381-396
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• 2003

Subsequence matching is an operation that finds subsequences whose changing patterns are similar to a given query sequence from time-series databases. This paper points out the performance bottleneck in subsequence matching, and then proposes an effective method that improves the performance of entire subsequence matching significantly by resolving the performance bottleneck. First, we analyze the disk access and CPU processing times required during the index searching and post processing steps through preliminary experiments. Based on their results, we show that the post processing step is the main performance bottleneck in subsequence matching, and them claim that its optimization is a crucial issue overlooked in previous approaches. In order to resolve the performance bottleneck, we propose a simple but quite effective method that processes the post processing step in the optimal way. By rearranging the order of candidate subsequences to be compared with a query sequence, our method completely eliminates the redundancy of disk accesses and CPU processing occurred in the post processing step. We formally prove that our method is optimal and also does not incur any false dismissal. We show the effectiveness of our method by extensive experiments. The results show that our method achieves significant speed-up in the post processing step 3.91 to 9.42 times when using a data set of real-world stock sequences and 4.97 to 5.61 times when using data sets of a large volume of synthetic sequences. Also, the results show that our method reduces the weight of the post processing step in entire subsequence matching from about 90% to less than 70%. This implies that our method successfully resolves th performance bottleneck in subsequence matching. As a result, our method provides excellent performance in entire subsequence matching. The experimental results reveal that it is 3.05 to 5.60 times faster when using a data set of real-world stock sequences and 3.68 to 4.21 times faster when using data sets of a large volume of synthetic sequences compared with the previous one.

• International Journal of High-Rise Buildings
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• v.9 no.2
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• pp.127-154
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• 2020

Disproportionate collapse triggered by local structural failure may cause huge casualties and economic losses, being one of the most critical civil engineering incidents. It is generally recognized that ensuring robustness of a structure, defined as its insensitivity to local failure, is the most acceptable and effective method to arrest disproportionate collapse. To date, the concept of robustness in its definition and quantification is still an issue of controversy. This paper presents a detailed review on about 50 quantitative measures of robustness for building structures, being classified into structural attribute-based and structural performance-based measures (deterministic and probabilistic). The definition of robustness is first described and distinguished from that of collapse resistance, vulnerability and redundancy. The review shows that deterministic measures predominate in quantifying structural robustness by comparing the structural responses of an intact and damaged structure. The attribute-based measures based on structural topology and stiffness are only applicable to elastic state of simple structural forms while the probabilistic measures receive growing interest by accounting for uncertainties in abnormal events, local failure, structural system and failure-induced consequences, which can be used for decision-making tools. There is still a lack of generalized quantifications of robustness, which should be derived based on the definition and design objectives and on the response of a structure to local damage as well as the associated consequences of collapse. Critical issues and recommendations for future design and research on quantification of robustness are provided from the views of column removal scenarios, types of structures, regularity of structural layouts, collapse modes, numerical methods, multiple hazards, degrees of robustness, partial damage of components, acceptable design criteria.

• Korean Journal of Remote Sensing
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• v.33 no.5_1
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• pp.455-467
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• 2017

When performing target detection using hyperspectral imagery, a feature extraction process is necessary to solve the problem of redundancy of adjacent spectral bands and the problem of a large amount of calculation due to high dimensional data. This study proposes a new band selection method using the $L_{2,1}$-norm regression model to apply the feature selection technique in the machine learning field to the hyperspectral band selection. In order to analyze the performance of the proposed band selection technique, we collected the hyperspectral imagery and these were used to analyze the performance of target detection with band selection. The Adaptive Cosine Estimator (ACE) detection performance is maintained or improved when the number of bands is reduced from 164 to about 30 to 40 bands in the 350 nm to 2500 nm wavelength band. Experimental results show that the proposed band selection technique extracts bands that are effective for detection in hyperspectral images and can reduce the size of the data without reducing the performance, which can help improve the processing speed of real-time target detection system in the future.