• Journal of Plant Biotechnology
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• v.49 no.3
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• pp.240-249
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• 2022

Measuring chlorophyll fluorescence (CF) is a useful tool for assessing a plant's ability to tolerate abiotic stresses such as drought, waterlogging and high temperature. Korean ginseng is highly sensitive to water stress in paddy fields. To evaluate the possibility of non-destructively diagnosing waterlogging stress using chlorophyll fluorescence (CF) imaging techniques, we screened 57 ginseng accessions for waterlogging tolerance. To evaluate waterlogging tolerance among the 2-year-old Korean ginseng accessions, we treated ginseng plants with water stress for 25 days. The physiological disorder rate was characterized through visual assessment (an assigned score of 0-5). The physiological disorder rates of Geumjin, Geumsun and GS00-58 were lower than that of other accessions. In contrast, lines GS97-62, GS97-69 and GS98-1-5 were deemed susceptible. Root traits, chlorophyll content and the reduction rates decreased in most ginseng accessions. Further, these metrics were significantly lower in susceptible genotypes compared to resistant ones. All CF parameters showed a positive or negative response to waterlogging stress, and this response continuously increased over the treatment time among the genotypes. The CF parameter Fv/Fm was used to screen the 57 accessions, and the results showed clear differences in Fv/Fm between the susceptible and resistant genotypes. Susceptible genotypes had an especially low Fv/Fm value of less than 0.8, reflecting damage to the reaction center of photosystem II. It is concluded that Fv/Fm can be used as a CF parameter index for screening waterlogging stress tolerance in ginseng genotypes.

• Journal of Oral Medicine and Pain
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• v.33 no.3
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• pp.279-294
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• 2008

This study aimed to make an analysis of the occlusion in the state of muscle fatigue produced by excessive mouth opening and clenching during the dental treatment to control the dental pain and to evaluate the sensory nerve in the muscle pain state. Most of the reasons why patients visit the dental office result in pain-either conceivably the dental origin pain or the non-dental origin pain. The dental offices have many therapeutic actions to produce the masticatory muscle fatigue for the treatment. Dental treatment with long minutes of mouth opening can cause some headaches, masticatory muscle pain and mouth opening difficulties. Patients with mastication problems who visits a dental office to alleviate pain run against another unexpected pain with other aspects. This study uses T-scan II system(Tekscan Co., USA) for the evaluation on the occlusal pattern in the experimental muscle fatigue after clenching, opening the mouth excessively and chewing gum. The occlusal contact pattern is analyzed by the contact timing, namely first, intercuspal, maximum and end point of contact. This inspection was performed at frequencies of 2000Hz, 250 Hz and 5 Hz before and after each experimental muscle pain was produced to 24 subjects who had normal occlusion without the orthodontic treatment or a wide range of the prosthesis by using $neurometer^{\circledR}$ CPT/C(Neurotron, Inc. Baltimore, Maryland, USA). The measuring sites were mandibular nerve experimental muscle fatigue respectively. This study could obtain the following results after the assessment of occlusion and sensory nerve of the experimental muscle fatigue. 1. There were the fastest expression after the excessive mouth opening in muscle fatigue and after tooth clenching in muscle pain. In the visual analog scale that records the subjective level, there was the highest scale after the clenching in the muscle fatigue in jumping off the point of pain. 2. Tooth contact time, contact force, relative contact force on the point of the first contact had no difference, and there were decreases in the contact force after the excessive mouth opening on intercuspal position point, after the excessive mouth opening and the gum chewing on the point of the maximum, and in the contact time after all the experimental muscle fatigue state on the point of the end contact. 3. There was no statistic significance in the current perception threshold before and after the experimental muscle fatigue. 4. There was no significant difference in the contact number, the maximal contact number on the point of the first contact, and the contact number after the mouth opening and gum chewing on the point of the intercuspal position and the contact number after the experimental muscle fatigue on the maximum point, and showed significant decreases. In conclusion, it was found that the occlusal pattern can cause the changes on the case of the clinical muscle weakness by intra-external oral events. It was important that the sedulous attention to details is required during dental treatment in case of excessive mouth opening, mastication and clenching.

• Information Systems Review
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• v.16 no.3
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• pp.161-177
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• 2014

Corporate credit rating assessment consists of complicated processes in which various factors describing a company are taken into consideration. Such assessment is known to be very expensive since domain experts should be employed to assess the ratings. As a result, the data-driven corporate credit rating prediction using statistical and artificial intelligence (AI) techniques has received considerable attention from researchers and practitioners. In particular, statistical methods such as multiple discriminant analysis (MDA) and multinomial logistic regression analysis (MLOGIT), and AI methods including case-based reasoning (CBR), artificial neural network (ANN), and multiclass support vector machine (MSVM) have been applied to corporate credit rating.2) Among them, MSVM has recently become popular because of its robustness and high prediction accuracy. In this study, we propose a novel optimized MSVM model, and appy it to corporate credit rating prediction in order to enhance the accuracy. Our model, named 'GAMSVM (Genetic Algorithm-optimized Multiclass Support Vector Machine),' is designed to simultaneously optimize the kernel parameters and the feature subset selection. Prior studies like Lorena and de Carvalho (2008), and Chatterjee (2013) show that proper kernel parameters may improve the performance of MSVMs. Also, the results from the studies such as Shieh and Yang (2008) and Chatterjee (2013) imply that appropriate feature selection may lead to higher prediction accuracy. Based on these prior studies, we propose to apply GAMSVM to corporate credit rating prediction. As a tool for optimizing the kernel parameters and the feature subset selection, we suggest genetic algorithm (GA). GA is known as an efficient and effective search method that attempts to simulate the biological evolution phenomenon. By applying genetic operations such as selection, crossover, and mutation, it is designed to gradually improve the search results. Especially, mutation operator prevents GA from falling into the local optima, thus we can find the globally optimal or near-optimal solution using it. GA has popularly been applied to search optimal parameters or feature subset selections of AI techniques including MSVM. With these reasons, we also adopt GA as an optimization tool. To empirically validate the usefulness of GAMSVM, we applied it to a real-world case of credit rating in Korea. Our application is in bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. The experimental dataset was collected from a large credit rating company in South Korea. It contained 39 financial ratios of 1,295 companies in the manufacturing industry, and their credit ratings. Using various statistical methods including the one-way ANOVA and the stepwise MDA, we selected 14 financial ratios as the candidate independent variables. The dependent variable, i.e. credit rating, was labeled as four classes: 1(A1); 2(A2); 3(A3); 4(B and C). 80 percent of total data for each class was used for training, and remaining 20 percent was used for validation. And, to overcome small sample size, we applied five-fold cross validation to our dataset. In order to examine the competitiveness of the proposed model, we also experimented several comparative models including MDA, MLOGIT, CBR, ANN and MSVM. In case of MSVM, we adopted One-Against-One (OAO) and DAGSVM (Directed Acyclic Graph SVM) approaches because they are known to be the most accurate approaches among various MSVM approaches. GAMSVM was implemented using LIBSVM-an open-source software, and Evolver 5.5-a commercial software enables GA. Other comparative models were experimented using various statistical and AI packages such as SPSS for Windows, Neuroshell, and Microsoft Excel VBA (Visual Basic for Applications). Experimental results showed that the proposed model-GAMSVM-outperformed all the competitive models. In addition, the model was found to use less independent variables, but to show higher accuracy. In our experiments, five variables such as X7 (total debt), X9 (sales per employee), X13 (years after founded), X15 (accumulated earning to total asset), and X39 (the index related to the cash flows from operating activity) were found to be the most important factors in predicting the corporate credit ratings. However, the values of the finally selected kernel parameters were found to be almost same among the data subsets. To examine whether the predictive performance of GAMSVM was significantly greater than those of other models, we used the McNemar test. As a result, we found that GAMSVM was better than MDA, MLOGIT, CBR, and ANN at the 1% significance level, and better than OAO and DAGSVM at the 5% significance level.

• Nuclear Medicine and Molecular Imaging
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• v.41 no.4
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• pp.291-298
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• 2007

Purpose: To investigate the feasibility of TI-201 SPECT with intra coronary injection (lC-I) in the detection of viable myocardium, we have performed SPECT imaging after direct intracoronary injection of TI-201 and images were compared with those of stress-reinjection (Re-I) SPECT. Methods: Fourteen coronary artery disease patients (male 11, mean age 54 years) who had myocardial infarction or demonstrated left ventricular wall motion abnormality on echocardiography were enrolled. Three mCi of TI-201 was injected into both coronary arteries during angiography and images were acquired between 6- and 24-hour after injection. Reinjection imaging with 1 mCi of TI-201 was performed at 4-hour after adenosine stress imaging with 3 mCi of TI-201. Images were interpreted according to 4-grade visual scoring system (grade 0-3). Segments with mild to moderated uptake (${\leq}$grade 1), and upgraded more than one score with reinjection, and were defined as viable myocardium. Results: Image quality was poor in two cases with IC-I. Numbers of non-viable segments were 60 (23.8%) with IC-I, and 38 (15.1%) with Re-I, respectively. Overall agreement for perfusion grade per myocardial segment in each IC-I and Re-I was 76.5%. Overall agreement for viable segment between IC-I and Re-I was 90.5%. Only one out of 38 segments interpreted as non-viable with Re-I were interpretated as viable with IC-I. And 23 out of 214 segments interpreted as viable with Re-I were interpreted as non-viable with IC-I. Conclusion: Intracoronary TI-201 SPECT seemed to be not advantageous over stress-rest reinjection imaging in the assessment of myocardial viability, mainly due to low count statistics at 6-hour or 24-hour delayed time points. The feasibility of intracoronary TI- 201 SPECT is considered to be limited.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.185-202
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• 2012

.Since the value of information has been realized in the information society, the usage and collection of information has become important. A facial expression that contains thousands of information as an artistic painting can be described in thousands of words. Followed by the idea, there has recently been a number of attempts to provide customers and companies with an intelligent service, which enables the perception of human emotions through one's facial expressions. For example, MIT Media Lab, the leading organization in this research area, has developed the human emotion prediction model, and has applied their studies to the commercial business. In the academic area, a number of the conventional methods such as Multiple Regression Analysis (MRA) or Artificial Neural Networks (ANN) have been applied to predict human emotion in prior studies. However, MRA is generally criticized because of its low prediction accuracy. This is inevitable since MRA can only explain the linear relationship between the dependent variables and the independent variable. To mitigate the limitations of MRA, some studies like Jung and Kim (2012) have used ANN as the alternative, and they reported that ANN generated more accurate prediction than the statistical methods like MRA. However, it has also been criticized due to over fitting and the difficulty of the network design (e.g. setting the number of the layers and the number of the nodes in the hidden layers). Under this background, we propose a novel model using Support Vector Regression (SVR) in order to increase the prediction accuracy. SVR is an extensive version of Support Vector Machine (SVM) designated to solve the regression problems. The model produced by SVR only depends on a subset of the training data, because the cost function for building the model ignores any training data that is close (within a threshold ${\varepsilon}$) to the model prediction. Using SVR, we tried to build a model that can measure the level of arousal and valence from the facial features. To validate the usefulness of the proposed model, we collected the data of facial reactions when providing appropriate visual stimulating contents, and extracted the features from the data. Next, the steps of the preprocessing were taken to choose statistically significant variables. In total, 297 cases were used for the experiment. As the comparative models, we also applied MRA and ANN to the same data set. For SVR, we adopted '${\varepsilon}$-insensitive loss function', and 'grid search' technique to find the optimal values of the parameters like C, d, ${\sigma}^2$, and ${\varepsilon}$. In the case of ANN, we adopted a standard three-layer backpropagation network, which has a single hidden layer. The learning rate and momentum rate of ANN were set to 10%, and we used sigmoid function as the transfer function of hidden and output nodes. We performed the experiments repeatedly by varying the number of nodes in the hidden layer to n/2, n, 3n/2, and 2n, where n is the number of the input variables. The stopping condition for ANN was set to 50,000 learning events. And, we used MAE (Mean Absolute Error) as the measure for performance comparison. From the experiment, we found that SVR achieved the highest prediction accuracy for the hold-out data set compared to MRA and ANN. Regardless of the target variables (the level of arousal, or the level of positive / negative valence), SVR showed the best performance for the hold-out data set. ANN also outperformed MRA, however, it showed the considerably lower prediction accuracy than SVR for both target variables. The findings of our research are expected to be useful to the researchers or practitioners who are willing to build the models for recognizing human emotions.