• Journal of the Korean Wood Science and Technology
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• v.34 no.6
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• pp.44-50
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• 2006

To characterize thermo-chemical feature of su gar conversion of woody biomass poplar wood (Populus alba${\times}$glandulosa ) by sub- and supercritical water was treated for 60s under subcritical (23 MPa, 325 and $350^{\circ}C$) and supercritical (23 MPa, 380, 400, and $425^{\circ}C$) conditions, respectively. Among degradation products undegraded poplar wood solids existed in aqueous products. As the treatment temperature increased, the degradation of poplar wood was enhanced and reached up to 83.1% at $425^{\circ}C$. The monomeric sugars derived from fibers of poplar wood by sub- and supercritical treatment were analyzed by high performance anionic exchange chromatography (HPAEC). Under the subcritical temperature ranges, xylan, main hemicellulose component in poplar wood, was preferentially degraded to xylose, while cellulose degradation started at the transition zone between sub and supercritical conditions and was remarkably accelerated at the supercritical condition. The highest yield of monomeric sugars amounts to ca. 7.3% based on air dried wood weight (MC 10%) at $425^{\circ}C$.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1244-1244
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from mastitic and healthy cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from mastitic and healthy cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA and FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference, thereby providing a useful means for spectroscopy-based clinic applications.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.1042-1042
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• 2001

In linear discriminant analysis there are two important properties concerning the effectiveness of discriminant function modeling. The first is the separability of the discriminant function for different classes. The separability reaches its optimum by maximizing the ratio of between-class to within-class variance. The second is the stability of the discriminant function against noises present in the measurement variables. One can optimize the stability by exploring the discriminant variates in a principal variation subspace, i. e., the directions that account for a majority of the total variation of the data. An unstable discriminant function will exhibit inflated variance in the prediction of future unclassified objects, exposed to a significantly increased risk of erroneous prediction. Therefore, an ideal discriminant function should not only separate different classes with a minimum misclassification rate for the training set, but also possess a good stability such that the prediction variance for unclassified objects can be as small as possible. In other words, an optimal classifier should find a balance between the separability and the stability. This is of special significance for multivariate spectroscopy-based classification where multicollinearity always leads to discriminant directions located in low-spread subspaces. A new regularized discriminant analysis technique, the principal discriminant variate (PDV) method, has been developed for handling effectively multicollinear data commonly encountered in multivariate spectroscopy-based classification. The motivation behind this method is to seek a sequence of discriminant directions that not only optimize the separability between different classes, but also account for a maximized variation present in the data. Three different formulations for the PDV methods are suggested, and an effective computing procedure is proposed for a PDV method. Near-infrared (NIR) spectra of blood plasma samples from daily monitoring of two Japanese cows have been used to evaluate the behavior of the PDV method in comparison with principal component analysis (PCA), discriminant partial least squares (DPLS), soft independent modeling of class analogies (SIMCA) and Fisher linear discriminant analysis (FLDA). Results obtained demonstrate that the PDV method exhibits improved stability in prediction without significant loss of separability. The NIR spectra of blood plasma samples from two cows are clearly discriminated between by the PDV method. Moreover, the proposed method provides superior performance to PCA, DPLS, SIMCA md FLDA, indicating that PDV is a promising tool in discriminant analysis of spectra-characterized samples with only small compositional difference.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.375-382
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• 2002

Handwritten character recognition is classified into on-line handwritten character recognition and off-line handwritten character recognition. On-line handwritten character recognition has made a remarkable outcome compared to off-line hacdwritten character recognition. This method can acquire the dynamic written information such as the writing order and the position of a stroke by means of pen-based electronic input device such as a tablet board. On the contrary, Any dynamic information can not be acquired in off-line handwritten character recognition since there are extreme overlapping between consonants and vowels, and heavily noisy images between strokes, which change the recognition performance with the result of the preprocessing. This paper proposes a method that effectively extracts the stroke including dynamic information of characters for off-line Korean handwritten character recognition. First of all, this method makes improvement and binarization of input handwritten character image as preprocessing procedure using watershed algorithm. The next procedure is extraction of skeleton by using the transformed Lu and Wang's thinning: algorithm, and segment pixel array is extracted by abstracting the feature point of the characters. Then, the vectorization is executed with a maximum permission error method. In the case that a few strokes are bound in a segment, a segment pixel array is divided with two or more segment vectors. In order to reconstruct the extracted segment vector with a complete stroke, the directional component of the vector is mortified by using right-hand writing coordinate system. With combination of segment vectors which are adjacent and can be combined, the reconstruction of complete stroke is made out which is suitable for character recognition. As experimentation, it is verified that the proposed method is suitable for handwritten Korean character recognition.

• Journal of the Korea Concrete Institute
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• v.28 no.5
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• pp.611-620
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• 2016

In the construction of nuclear power plants, only 420 MPa reinforcing bars are allowed and, therefore, so many large-diameter bars are placed, which results in steel congestion. Consequently, re-bar works are difficult and the quality of RC structures may be deteriorated. To solve the steel congestion, 550 MPa bars are necessary. Among many items for verifying structural performance of reinforced concrete with 550 MPa bars, the 43 mm hooked bars are examined in this study. All specimens failed by side-face blowout and the side cover explosively spalled at maximum loads. The bar force was initially transferred to the concrete primarily by bond along a straight portion. At the one third of maximum load, the bond reached a peak capacity and began to decline, while the hook bearing component rose rapidly. At failure, most load was resisted by the hook bearing. For confined specimens with hoops, the average value of test-to-prediction ratios by KCI code is 1.45. The modification factor of confining reinforcement which was not allowed for larger than 35 mm bars can be applied to 43 mm hooked bars. For specimens with 70 MPa concrete, the average value of test-to-prediction ratios by KCI code is 1.0 which is less than the values of the other specimens. The effects of concrete compressive strength should be reduced. An equation to predict anchorage capacity of hooked bars was developed from regression analysis including the effects of compressive strength of concrete, embedment length, side cover thickness, and transverse reinforcement index.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.3
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• pp.14-20
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• 2018

Electric vehicles are environmentally friendly because they emit no exhaust gas, unlike gasoline automobiles. However, since they are driven by the electric power from batteries, the distance they can travel based on a single charge depends on their energy density. Therefore, the lithium-ion battery having a high energy density is a good candidate for the batteries of electric vehicles. Since the electrode is an essential component that governs their efficiency, the electrode manufacturing process plays a vital role in the entire production process of lithium-ion batteries. In particular, the coating process is a critical step in the manufacturing of the electrode, which has a significant influence on its performance. In this paper, we propose an innovative process for improving the efficiency and productivity of the coating process in electrode manufacturing and describe the equipment design method and development results. Specifically, we propose a design procedure and development method in order to improve the core plate coating quality by 25%, using a technology capable of reducing the assembly margin due to its high output/high capacity and improving the product capacity quality and assembly process yield. Using this method, the battery life of the lithium-ion battery cell was improved. Compared with the existing coating process, the target loading level is maintained and dispersed to maintain the anode capacity (${\pm}0.4{\rightarrow}{\pm}0.3mg/cm^2r$ reduction).

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.423-430
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• 2013

Recently, the personal identification technologies using vein pattern of back of the hand, palm, and finger have been developed actively because it has the advantage that the vein blood vessel in the body is impossible to damage, make a replication and forge. However, it is difficult to extract clearly the vein region from captured vein images through common image prcessing based region segmentation method, because of the light scattering and non-uniform internal tissue by skin layer and inside layer skeleton, etc. Especially, it takes a long time for processing time and makes a discontinuity of blood vessel just in a image because it has non-uniform illumination due to use a locally different adaptive threshold for the binarization of acquired finger-vein image. To solve this problem, we propose illumination normalization based fast method for extracting the finger-vein region. The proposed method has advantages compared to the previous methods as follows. Firstly, for remove a non-uniform illumination of the captured vein image, we obtain a illumination component of the captured vein image by using a low-pass filter. Secondly, by extracting the finger-vein path using one time binarization of a single threshold selection, we were able to reduce the processing time. Through experimental results, we confirmed that the accuracy of extracting the finger-vein region was increased and the processing time was shortened than prior methods.

• Journal of Korea Water Resources Association
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• v.44 no.8
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• pp.667-681
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• 2011

The objective of this study is to evaluate the applicability of a Land Surface Model (LSM) for drought analysis in Korea. For evaluating the applicability of the model, the model was calibrated on several upper dam site watersheds and the hydrological components (runoff and soil moisture) were simulated over the whole South Korea at grid basis. After converting daily series of runoff and soil moisture data to accumulated time series (3, 6, 12 months), drought indices such as SRI and SSI are calculated through frequency analysis and standardization of accumulated probability. For evaluating the drought indices, past drought events are investigated and drought indices including SPI and PDSI are used for comparative analysis. Temporal and spatial analysis of the drought indices in addition to hydrologic component analysis are performed to evaluate the reproducibility of drought severity as well as relieving of drought. It can be concluded that the proposed indices obtained from the LSM model show good performance to reflect the historical drought events for both spatially and temporally. From this point of view, the LSM can be useful for drought management. It leads to the conclusion that these indices are applicable to domestic drought and water management.

• Journal of Animal Science and Technology
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• v.60 no.2
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• pp.1.1-1.6
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• 2018

Background: Maize fodder is being used as staple feed for livestock but it lacks protein and essential amino acids; lysine and tryptophan. Intercropping maize with leguminous soybean crop is promising technique under limited land resources of South Korea but it can only give considerable advantages when adequate distance is provided between corn and soybean rows. Main aim of present study was to find-out adequate distance between corn and soybean seeding rows for optimum growth, yield and nutritive value of intercropped forage. Methods: Different interrow distances between corn and soybean were evaluated under four treatments, viz. 1) Corn sole as positive control treatment 2) Zero cm between corn and soybean (control); 2) Five cm between corn and soybean; 3) 10 cm between corn and soybean, with three replicates under randomized block design. Results: Findings depicted that height and number of corn stalks and ears were similar (P > 0.05) among different treatments. Numerically average corn ear height was decreased at zero cm distance. Dry matter percentage in all components; corn stalk, corn ear and soybean was also found not different (P > 0.05) but dry matter yield in component of corn ear was lower (P < 0.05) at zero cm distance as compared to that of 5 and 10 cm interrow distances. In case of nutritive value, total digestible nutrient yield in intercropped corn was also found lower (P < 0.05) at zero cm distance than that of 5 and 10 cm interrow distances between corn and soybean seeding rows. Substantial decrease in dry matter yield of maize ear at zero cm distance might be attributed to factor of closed interrow spacing which made interplant competition more intensified for light interception, necessary for photosynthetic activity. Lower dry matter yield in ear also reduced total digestible nutrients in intercropped maize because it was determining factor in calculation of digestible nutrients. The optimum yield and nutritive value of forage at wider interrow distance i.e. 5 cm between corn and soybean might be due to adequate interseed distance. Conclusion: Conclusively, pattern of corn and soybean seeding in rows at 5 cm distance was found suitable which provided adequate interrow distance to maintain enough mutual cooperation and decreased competition between both species for optimum production performance and nutritive value of intercropped forage.

• Korean Journal of Food Science and Technology
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• v.47 no.5
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• pp.680-685
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• 2015

Phytochemicals in Codonopsis lanceolata leaves were saponins, triterpenes, tannins, and flavonoids, not alkaloids. The levels of total polyphenols and flavonoids in Codonopsis lanceolata leaves were measured to evaluate the antioxidant activity. C. lanceolata leaves showed strong 2, 2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and potent reducing power. In addition, C. lanceolata leaf extracts inhibited production of nitric oxide (NO) in lipopolysaccharide (LPS)-activated RAW 264.7 cells. To examine active phytochemical for antioxidant activity, aglycone fraction of C. lanceolata leaves was analyzed by high performance liquid chromatography (HPLC). Luteolin was identified as a main component of aglycone fraction and showed potent antioxidant activity as determined by a DPPH radical scavenging assay and reducing power test. These results suggest that C. lanceolata leaves are a good source of antioxidants.