• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.9
• /
• pp.127-135
• /
• 2013

In order to acquire images in low-light environments, it is usually necessary to adopt long exposure times or resort to flash lights. However, flashes often induce color distortion, cause the red-eye effect and can be disturbing to subjects. On the other hand, long-exposure shots are susceptible to subject-motion, as well as motion-blur due to camera shake when performed hand-held. A recently introduced technique to overcome the limitations of traditional low-light photography is that of multi-spectral flash. Multi-spectral flash images are a combination of UV/IR and visible spectrum information. The general idea is that of retrieving details from the UV/IR spectrum and color from the visible spectrum. However, multi-spectral flash images themselves are subject to color distortion and noise. This works presents a method to compute multi-spectral flash images so that noise can be reduced and color accuracy improved. The proposed approach is a previously seen optimization method, improved by the introduction of a weight map used to discriminate uniform regions from detail regions. The weight map is generated by applying canny edge operator and it is applied to the optimization process for discriminating the weights in uniform region and edge. Accordingly, the weight of color information is increased in the uniform region and the detail region of weight is decreased in detail region. Therefore, the proposed method can be enhancing color reproduction and removing artifacts. The performance of the proposed method has been objectively evaluated using long-exposure shots as reference.

• Asian Pacific Journal of Cancer Prevention
• /
• v.15 no.15
• /
• pp.6283-6286
• /
• 2014

In patients with microscopic hematuria there is a need for better identification of those who are at greater risk of harbouring bladder tumors. The RisikoCheck(C) questionnaire has a strong correlation with the presence of urothelial carcinoma (UC) of the bladder and in combination with other available tests may help identify patients who require detailed clinical investigations due to increased risk of presence of bladder tumors. This study aimed to evaluate the efficacy of RisikoCheck(C) questionnaire together with NMP-22(R) (BladderChek(R)) as a point-of-care urine test in predicting the presence of bladder tumors in patients presenting with microscopic hematuria as the sole finding. In this multi-institutional prospective evaluation of 303 consecutive patients without a history of urothelial carcinoma (UC), RisikoCheck(C) risk group assessment, urinary tract imaging and cystourethroscopy as well as urine cytology and Nuclear Matrix Protein-22 (NMP-22 BladderChek) testing were performed where available. The sensitivity, specificity, negative predictive value (NPV), and positive predictive values (PPV) for the risk adapted approach were calculated. All patients underwent cystoscopy, and tumors were detected in 18 (5.9%). Urine cytology and NMP-22 was positive for malignancy in 9 (3.2%) and 12 (7.5%) of patients, respectively. A total of 43 (14%) patients were in the high risk group according to the RisikoCheck(C) questionnaire. The sensitivity and specificity of the questionnaire in detecting a bladder tumor was 61.5 % and 84.0 % in the high risk group. In patients with either a positive NMP-22 test or high risk category RisikoCheck(C), 23.6% had bladder tumors with a corresponding sensitivity of 54.2% and specificity of 88.6%. If both tests were negative only 3.3% of the patients had bladder tumors. The results of our study suggest that the efficacy of diagnostic evaluation of patients with microscopic hematuria may be further enhanced by combining RisikoCheck(C) questionnaire with NMP-22.

• Information Systems Review
• /
• v.17 no.1
• /
• pp.49-64
• /
• 2015

Customer reviews which include subjective opinions for the product or service in online store have been generated rapidly and their influence on customers has become immense due to the widespread usage of SNS. In addition, a number of studies have focused on opinion mining to analyze the positive and negative opinions and get a better solution for customer support and sales. It is very important to select the key terms which reflected the customers' sentiment on the reviews for opinion mining. We proposed a document-level terms-based sentiment classification model by select in the optimal terms with part of speech tag. SVMs (Support vector machines) are utilized to build a predictor for opinion mining and we used the combination of POS tag and four terms extraction methods for the feature selection of SVM. To validate the proposed opinion mining model, we applied it to the customer reviews on Amazon. We eliminated the unmeaning terms known as the stopwords and extracted the useful terms by using part of speech tagging approach after crawling 80,000 reviews. The extracted terms gained from document frequency, TF-IDF, information gain, chi-squared statistic were ranked and 20 ranked terms were used to the feature of SVM model. Our experimental results show that the performance of SVM model with four POS tags is superior to the benchmarked model, which are built by extracting only adjective terms. In addition, the SVM model based on Chi-squared statistic for opinion mining shows the most superior performance among SVM models with 4 different kinds of terms extraction method. Our proposed opinion mining model is expected to improve customer service and gain competitive advantage in online store.

• Journal of Korean Society of Steel Construction
• /
• v.19 no.1
• /
• pp.29-41
• /
• 2007

Structural design, like other complex decision problems, involves many trade-offs among competing criteria. Although mathematical programming models are becoming increasingly realistic, they often have design limitations, that is, there are often relevant issues that cannot be easily captured. From the understanding of these limitations, a decision-support system is developed that can generate some useful alternatives as well as a single optimum value in the optimization of steel frame structures. The alternatives produced using this system are "good" with respect to modeled objectives, and yet are "different," and are often better, with respect to interesting objectives not present in the model. In this study, we created a decision-support system for designing the most cost-effective moment-resisting steel frame structures for resisting lateral loads without compromising overall stability. The proposed approach considers the cost of steel products and the cost of connections within the design process. This system makes use of an optimization formulation, which was modified to generate alternatives of optimum value, which is the result of the trade-off between the number of moment connections and total cost. This trade-off was achieved by reducing the number of moment connections and rearranging them, using the combination of analysis based on the LRFD code and optimization scheme based on genetic algorithms. To evaluate the usefulness of this system, the alternatives were examined with respect to various design aspects.

• Applied Chemistry for Engineering
• /
• v.25 no.2
• /
• pp.121-133
• /
• 2014

Block copolymers including ABA triblock architectures are useful as thermoplastic elastomers and toughened plastics depending on the relative glassy and rubbery content. These materials can be blended with other polymers and utilized as additives, toughening agents, and compatibilizers. Most of commercially available block copolymers are derived from petroleum. Renewable alternatives are attractive considering the finite supply of fossil resources on earth and the overall economic and environmental expenses involved in the recovery and use of oil. Furthermore, tomorrow's sustainable materials are demanding the design and implementation with programmed end-of-life. The present review focuses on the preparation and evaluation of new classes of renewable ABA triblock copolymers and also emphasizes on the use of carbohydrate-derived poly(lactide) or plant-based poly(olefins) having a high glass transition temperature and/or high melting temperature for the hard phase in addition to the use of bio-based amorphous hydrocarbon polymers with a low glass transition temperature for the soft components. The combination of multiple controlled polymerizations has proven to be a powerful approach. Precision-controlled synthesis of these hybrid macromolecules has led to the development of new elastomers and tough plastics offering renewability, biodegradability, and high performance.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.22 no.3
• /
• pp.75-87
• /
• 1980

Most hydro]ogic phenomena are the complex and organic products of multiple causations like climatic and hydro-geological factors. A certain significant correlation on the run-off in river basin would be expected and foreseen in advance, and the effect of each these causual and associated factors (independant variables; present-month rainfall, previous-month run-off, evapotranspiration and relative humidity etc.) upon present-month run-off(dependent variable) may be determined by multiple regression analysis. Functions between independant and dependant variables should be treated repeatedly until satisfactory and optimal combination of independant variables can be obtained. Reliability of the estimated function should be tested according to the result of statistical criterion such as analysis of variance, coefficient of determination and significance-test of regression coefficients before first estimated multiple regression model in historical sequence is determined. But some error between observed and estimated run-off is still there. The error arises because the model used is an inadequate description of the system and because the data constituting the record represent only a sample from a population of monthly discharge observation, so that estimates of model parameter will be subject to sampling errors. Since this error which is a deviation from multiple regression plane cannot be explained by first estimated multiple regression equation, it can be considered as a random error governed by law of chance in nature. This unexplained variance by multiple regression equation can be solved by stochastic approach, that is, random error can be stochastically simulated by multiplying random normal variate to standard error of estimate. Finally hybrid model on estimation of monthly run-off in nonhistorical sequence can be determined by combining the determistic component of multiple regression equation and the stochastic component of random errors. Monthly run-off in Naju station in Yong-San river basin is estimated by multiple regression model and hybrid model. And some comparisons between observed and estimated run-off and between multiple regression model and already-existing estimation methods such as Gajiyama formula, tank model and Thomas-Fiering model are done. The results are as follows. (1) The optimal function to estimate monthly run-off in historical sequence is multiple linear regression equation in overall-month unit, that is; Qn=0.788Pn+0.130Qn-1-0.273En-0.1 About 85% of total variance of monthly runoff can be explained by multiple linear regression equation and its coefficient of determination (R2) is 0.843. This means we can estimate monthly runoff in historical sequence highly significantly with short data of observation by above mentioned equation. (2) The optimal function to estimate monthly runoff in nonhistorical sequence is hybrid model combined with multiple linear regression equation in overall-month unit and stochastic component, that is; Qn=0. 788Pn+0. l30Qn-1-0. 273En-0. 10+Sy.t The rest 15% of unexplained variance of monthly runoff can be explained by addition of stochastic process and a bit more reliable results of statistical characteristics of monthly runoff in non-historical sequence are derived. This estimated monthly runoff in non-historical sequence shows up the extraordinary value (maximum, minimum value) which is not appeared in the observed runoff as a random component. (3) "Frequency best fit coefficient" (R2f) of multiple linear regression equation is 0.847 which is the same value as Gaijyama's one. This implies that multiple linear regression equation and Gajiyama formula are theoretically rather reasonable functions.

• Journal of Plant Biotechnology
• /
• v.37 no.1
• /
• pp.12-24
• /
• 2010

Plant metabolomics is a research field for identifying all of the metabolites found in a certain plant cell, tissue, organ, or whole plant in a given time and conditions and for studying changes in metabolic profiling as time goes or conditions change. Metabolomics is one of the most recently developed omics for holistic approach to biology and is a kind of systems biology. Metabolomics or metabolite fingerprinting techniques usually involves collecting spectra of crude solvent extracts without purification and separation of pure compounds or not in standardized conditions. Therefore, that requires a high degree of reproducibility, which can be achieved by using a standardized method for sample preparation and data acquisition and analysis. In plant biology, metabolomics is applied for various research fields including rapid discrimination between plant species, cultivar and GM plants, metabolic evaluation of commercial food stocks and medicinal herbs, understanding various physiological, stress responses, and determination of gene functions. Recently, plant metabolomics is applied for characterization of gene function often in combination with transcriptomics by analyzing tagged mutants of the model plants of Arabidopsis and rice. The use of plant metabolomics combined by transcriptomics in functional genomics will be the challenge for the coming year. This review paper attempted to introduce current status and prospects of plant metabolomics research.

• Proceedings of the Korean Vacuum Society Conference
• /
• 2011.02a
• /
• pp.407-408
• /
• 2011

Natural boron consists of two stable isotopes 10B and 11B with natural abundance of 18.8 atom percent of 10B and 81.2 atom percent of 11B. The thermal neutron absorption cross-section for 10B and 11B are 3837 barn and 0.005 barn respectively. 10B enriched specific compounds are used for control rods and as a reactor coolant additives. In this work 2 methods for boron enrichment were analysed: 1) Gas irradiation in static conditions. Dissociation occurs due to multiphoton absorption by specific isotopes in appropriately tuned laser field. IR shifted laser pulses are usually used in combination with increasing the laser intensity also improves selectivity up to some degree. In order to prevent recombination of dissociated molecules BCl3 is mixed with H2S 2) SILARC method. Advantages of this method: a) Gas cooling is helpful to split and shrink boron isotopes absorption bands. In order to achieve better selectivity BCl3 gas has to be substantially rarefied (~0.01%-5%) in mixture with carrier gas. b) Laser intensity is lower than in the first method. Some preliminary calculations of dissociation and recombination with carrier gas molecules energetics for both methods will be demonstrated Boron separation in SILARC method can be represented as multistage process: 1) Mixture of BCl3 with carrier gas is putted in reservoir 2) Gas overcooling due to expansion through Laval nozzle 3) IR multiphoton absorption by gas irradiated by specifically tuned laser field with subsequent gradual gas condensation in outlet chamber It is planned to develop software which includes these stages. This software will rely on the following available software based on quantum molecular dynamics in external quantized field: 1) WavePacket: Each particle is treated semiclassicaly based on Wigner transform method 2) Turbomole: It is based on local density methods like density of functional methods (DFT) and its improvement- coupled clusters approach (CC) to take into account quantum correlation. These models will be used to extract information concerning kinetic coefficients, and their dependence on applied external field. Information on radiative corrections to equation of state induced by laser field which take into account possible phase transition (or crossover?) can be also revealed. This mixed phase equation of state with quantum corrections will be further used in hydrodynamical simulations. Moreover results of these hydrodynamical simulations can be compared with results of CFD calculations. The first reasonable question to ask before starting the CFD simulations is whether turbulent effects are significant or not, and how to model turbulence? The questions of laser beam parameters and outlet chamber geometry which are most optimal to make all gas volume irradiated is also discussed. Relationship between enrichment factor and stagnation pressure and temperature based on experimental data is also reported.

• Journal of the Institute of Convergence Signal Processing
• /
• v.19 no.1
• /
• pp.7-13
• /
• 2018

In the current study, we proposed an optimized brain-computer interface (BCI) which employed blind source separation (BBS) approach to remove noises. Thus motor imagery (MI) signal and steady state visual evoked potential (SSVEP) signal were easily to be detected due to enhancement in signal-to-noise ratio (SNR). Moreover, a combination between MI and SSVEP which is typically can increase the number of commands being generated in the current BCI. To reduce the computational time as well as to bring the BCI closer to real-world applications, the current system utilizes a single-channel EEG signal. In addition, a convolutional neural network (CNN) was used as the multi-class classification model. We evaluated the performance in term of accuracy between a non-BBS+BCI and BBS+BCI. Results show that the accuracy of the BBS+BCI is achieved $16.15{\pm}5.12%$ higher than that in the non-BBS+BCI by using BBS than non-used on. Overall, the proposed BCI system demonstrate a feasibility to be applied for multi-dimensional control applications with a comparable accuracy.

• Journal of the Korean Association of Oral and Maxillofacial Surgeons
• /
• v.39 no.6
• /
• pp.274-282
• /
• 2013

Objectives: The posterior maxillary region often provides a limited bone volume for dental implants. Maxillary sinus elevation via inserting a bone graft through a window opened in the lateral sinus wall has become the most common surgical procedure for increasing the alveolar bone height in place of dental implants in the posterior maxillary region. The purpose of this article is to assess the change of bone volume and the clinical effects of dental implant placement in sites with maxillary sinus floor elevation and autogenous bone graft through the lateral window approach. Materials and Methods: In this article, the analysis data were collected from 64 dental implants that were placed in 24 patients with 29 lacks of the bone volume posterior maxillary region from June 2004 to April 2011, at the Department of Oral and Maxillofacial Surgery, Inha University Hospital. Panoramic views were taken before the surgery, after the surgery, 6 months after the surgery, and at the time of the final follow-up. The influence of the factors on the grafted bone material resorption rate was evaluated according to the patient characteristics (age and gender), graft material, implant installation stage, implant size, implant placement region, local infection, surgical complication, and residual alveolar bone height. Results: The bone graft resorption rate of male patients at the final follow-up was significantly higher than the rate of female patients. The single autogenous bone-grafted site was significantly more resorbed than the autogenous bone combined with the Bio-Oss grafted site. The implant installation stage and residual alveolar height showed a significant correlation with the resorption rate of maxillary sinus bone graft material. The success rate and survival rate of the implant were 92.2% and 100%, respectively. Conclusion: Maxillary sinus elevation procedure with autogenous bone graft or autogenous bone in combination with Bio-Oss is a predictable treatment method for implant rehabilitation.