• Journal of Intelligence and Information Systems
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• v.19 no.4
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• pp.55-79
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• 2013

In this paper, we propose a hybrid genetic algorithm (HGA) approach to effectively solve the reverse logistics network with centralized centers (RLNCC). For the proposed HGA approach, genetic algorithm (GA) is used as a main algorithm. For implementing GA, a new bit-string representation scheme using 0 and 1 values is suggested, which can easily make initial population of GA. As genetic operators, the elitist strategy in enlarged sampling space developed by Gen and Chang (1997), a new two-point crossover operator, and a new random mutation operator are used for selection, crossover and mutation, respectively. For hybrid concept of GA, an iterative hill climbing method (IHCM) developed by Michalewicz (1994) is inserted into HGA search loop. The IHCM is one of local search techniques and precisely explores the space converged by GA search. The RLNCC is composed of collection centers, remanufacturing centers, redistribution centers, and secondary markets in reverse logistics networks. Of the centers and secondary markets, only one collection center, remanufacturing center, redistribution center, and secondary market should be opened in reverse logistics networks. Some assumptions are considered for effectively implementing the RLNCC The RLNCC is represented by a mixed integer programming (MIP) model using indexes, parameters and decision variables. The objective function of the MIP model is to minimize the total cost which is consisted of transportation cost, fixed cost, and handling cost. The transportation cost is obtained by transporting the returned products between each centers and secondary markets. The fixed cost is calculated by opening or closing decision at each center and secondary markets. That is, if there are three collection centers (the opening costs of collection center 1 2, and 3 are 10.5, 12.1, 8.9, respectively), and the collection center 1 is opened and the remainders are all closed, then the fixed cost is 10.5. The handling cost means the cost of treating the products returned from customers at each center and secondary markets which are opened at each RLNCC stage. The RLNCC is solved by the proposed HGA approach. In numerical experiment, the proposed HGA and a conventional competing approach is compared with each other using various measures of performance. For the conventional competing approach, the GA approach by Yun (2013) is used. The GA approach has not any local search technique such as the IHCM proposed the HGA approach. As measures of performance, CPU time, optimal solution, and optimal setting are used. Two types of the RLNCC with different numbers of customers, collection centers, remanufacturing centers, redistribution centers and secondary markets are presented for comparing the performances of the HGA and GA approaches. The MIP models using the two types of the RLNCC are programmed by Visual Basic Version 6.0, and the computer implementing environment is the IBM compatible PC with 3.06Ghz CPU speed and 1GB RAM on Windows XP. The parameters used in the HGA and GA approaches are that the total number of generations is 10,000, population size 20, crossover rate 0.5, mutation rate 0.1, and the search range for the IHCM is 2.0. Total 20 iterations are made for eliminating the randomness of the searches of the HGA and GA approaches. With performance comparisons, network representations by opening/closing decision, and convergence processes using two types of the RLNCCs, the experimental result shows that the HGA has significantly better performance in terms of the optimal solution than the GA, though the GA is slightly quicker than the HGA in terms of the CPU time. Finally, it has been proved that the proposed HGA approach is more efficient than conventional GA approach in two types of the RLNCC since the former has a GA search process as well as a local search process for additional search scheme, while the latter has a GA search process alone. For a future study, much more large-sized RLNCCs will be tested for robustness of our approach.

• 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.

• The Journal of Korean Academy of Prosthodontics
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• v.46 no.5
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• pp.443-454
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• 2008

Statement of problem: A clinically successful color match is one of the important factor to get an esthetic dental restoration. Dental shade guides are commonly used to evaluate tooth color in restorative procedure. But numerous reports have indicated that common shade guides do not provide sufficient spectral coverage of the natural tooth colors. To address issues associated with the shade guide, distinct avenues have been pursued objective spectrophotometric / colorimetric assessment. Purpose: This study compared the accuracy of tooth color selection of spectrophotometer with that of human visual determination. Three main factors were investigated, namely, the effect of light, the individual variation and the experience of the observer. Material and methods: At the first experiment, on ten patients, one operator independently selected the best matching shade to the unrestored maxillary central incisor, using a Vita Classical Shade Guide in the morning, at noon and in the afternoon. The same teeth were measured by means of a reflectance spectrophotometer. At the second experiment, on ten patients, ten operators (5 experts, 5 novices) selected and measured by the same method above at noon. At the third experiment, the results of the second experiment were divided into two groups, expert and novice, and analyzed. Results: 1. There was significant difference between visual and spectrophotometric assessment (mean ${\Delta}E$ values) in experiment 1, 2, 3 (P < .05). 2. There was no significant difference between experts and novices group, when comparing with each visual and spectrophotometric assessment (mean ${\Delta}E$ values). Conclusion: Spectrophotometer could be used to analyze the shade of natural tooth objectively. Thereby, this method offers the potential tominimize considerably the need for corrections or even remakesafter intraoral try-in of restoration. Furthermore, to achieve its advantage, both the shade-matching environment and communication between dentist and technician should be optimized with use of visual and instrumental shade-matching systems.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.33 no.6
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• pp.2551-2562
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• 2013

Since 2006, an Intelligent Excavation Robot which automatically performs the earth-work without operator has been developed in Korea. The technologies for automatically recognizing the terrain of work environment and detecting the objects such as obstacles or dump trucks are essential for its work quality and safety. In several countries, terrestrial 3D laser scanner and stereo vision camera have been used to model the local area around workspace of the automated construction equipment. However, these attempts have some problems that require high cost to make the sensor system or long processing time to eliminate the noise from 3D model outcome. The objectives of this study are to analyze the advantages of the existing 3D modeling sensors and to examine the applicability for practical use by using Analytic Hierarchical Process(AHP). In this study, 3D modeling quality and accuracy of modeling sensors were tested at the real earth-work environment.

• Journal of Korean Dental Science
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• v.6 no.2
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• pp.67-77
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• 2013

Purpose: The purpose of this study was to evaluate the survival and success rates of Korean Osstem implants US II Plus, GS II following loading period. Materials and Methods: Dental records were obtained in total 201 patients who were treated with Korean Osstem implants US II Plus, GS II on both maxillary and mandibular anterior and posterior areas in six different clinics for 2 years from January 2007 to December 2008. Total 430 implants were evaluated clinically and radiographically using predefined success criteria prospectively and following results were obtained. Result: US II Plus, GS II implants showed high survival rates of more than 99% and high success rates more than 90% independent of loading period. As a result of cross analysis to evaluate clinical significance between implant loading period and success rate, the P-value of US II Plus was 0.10 (P>0.05), and the P-value of GS II was 0.17 (P>0.05), which showed no statistical significance. Bone quality, smoking, and edentulous state are factors that can affect the survival and success rates following differently loaded implants, but did not significantly affect in this study. Conclusion: These results suggest that selection of loading period of Korean Osstem implants US II Plus, GS II would be done carefully considering implant install area, the quality alveolar bone, the state of edentulous ridge and experience of operator, though they showed clinically good results on both maxillary and mandibular anterior and posterior areas.

• Journal of the korean academy of Pediatric Dentistry
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• v.35 no.1
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• pp.110-117
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• 2008

Clinical performance of resin-based composite material depends on case selection and on the proficiency of the operator. Especially, composite resin restoration on permanent first molar in children have more limitations clinically than adult patients according to stage of tooth eruption and patient's compliance. This study was designed to evaluate the clinical performance of posterior composite resin restoration in children after 5 years. 35 teeth of 16 patients who were received composite resin restoration on permanent first molar in Department of Pediatric dentistry, Pusan National University Hospital between January 2001 and December 2001 were evaluated based on Modified USPHS criteria. From the finding in this study, following conclusions can be made. 1. 6 teeth(17%) of 35 teeth was replaced, so 5-years survival rate of posterior composite resin restoration is 82.9%. 2. As results of each evaluation criteria, on color match, anatomic form, surface roughness, sensitivity/ discomfort, ideal A grade score was 86.2%, 93.1%, 86.2%, 86.2%, clinically accepted B grade score was 13.8%, 0%, 13.8%, 10.3%. On marginal adaptation and marginal discoloration, A grade score was 13.8%, 44.8% and B grade score was 79.3%, 34.5% and secondary caries rate was 20.7%. 3. 69.1% of teeth (20 teeth) was clinically accepted on all evaluation criteria.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.10
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• pp.6019-6023
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• 2013

Lung cancer, one of the leading causes of cancer-related deaths, usually appears as solitary pulmonary nodules (SPNs) which are hard to diagnose using the naked eye. In this paper, curvelet-based textural features and clinical parameters are used with three prediction models [a multilevel model, a least absolute shrinkage and selection operator (LASSO) regression method, and a support vector machine (SVM)] to improve the diagnosis of benign and malignant SPNs. Dimensionality reduction of the original curvelet-based textural features was achieved using principal component analysis. In addition, non-conditional logistical regression was used to find clinical predictors among demographic parameters and morphological features. The results showed that, combined with 11 clinical predictors, the accuracy rates using 12 principal components were higher than those using the original curvelet-based textural features. To evaluate the models, 10-fold cross validation and back substitution were applied. The results obtained, respectively, were 0.8549 and 0.9221 for the LASSO method, 0.9443 and 0.9831 for SVM, and 0.8722 and 0.9722 for the multilevel model. All in all, it was found that using curvelet-based textural features after dimensionality reduction and using clinical predictors, the highest accuracy rate was achieved with SVM. The method may be used as an auxiliary tool to differentiate between benign and malignant SPNs in CT images.

• The Korean Journal of Mycology
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• v.46 no.4
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• pp.479-490
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• 2018

Red pepper is seriously damaged by thrips (Thrips palmi) and anthracnose caused by Colletotrichum acutatum throughout its development. Because of biotic constraints, producers often depend on chemicals that are expensive and have adverse effects on the environment, operator, and beneficial insects. In addition, resistance is developed because of the repeated use of chemicals. In recent decades, the use of microorganisms in crop protection has become a credible alternative because it is eco-friendly. In this study, we aimed to select isolates with insecticidal and fungicidal activities against the pathogens that cause anthracnose and thrips. We treated T. palmi adults and juveniles with 13 strains of entomopathogenic fungi (isolated from the soil by using the insect-bait method), and 6 strains showed excellent insecticidal activity (70-100%) 5 days after the treatment. The selected isolates were cultured with C. acutatum to screen for the strain with excellent anti-fungal activities, among which an isolate FT333 showed more than 95% control efficacy against C. acutatum in vitro. The isolate was identified as Isaria javanica through its morphological characteristics and phylogenetic analysis of the ITS and ${\beta}-tubulin$ nucleotide sequences. The Isaria javanica FT333 isolate could be used effectively for dual bio-control of thrips and anthracnose during red pepper cultivation.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.3
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• pp.217-226
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• 2022

In the view of the application of high-precision spatial information production, UAV (Umanned Aerial Vehicle)-Photogrammetry has a problem in that it lacks specific procedures and detailed regulations for quantitative quality verification methods or certification of captured images. In addition, test tools for UAV image quality assessment use only the GSD (Ground Sample Distance), not MTF (Modulation Transfer Function), which reflects image resolution and contrast at the same time. This fact makes often the quality of UAV image inferior to that of manned aerial image. We performed MTF and GSD analysis simultaneously using a siemens star to confirm the necessity of MTF analysis in UAV image quality assessment. The analyzing results of UAV images taken with different payload and sensors show that there is a big difference in σ_MTF values, representing image resolution and the degree of contrast, but slightly different in GSD. It concluded that the MTF analysis is a more objective and reliable analysis method than just the GSD analysis method, and high-quality drone images can only be obtained when the operator make images after judging the proper selection the sensor performance, image overlaps, and payload type. However, the results of this study are derived from analyzing only images acquired by limited sensors and imaging conditions. It is therefore expected that more objective and reliable results will be obtained if continuous research is conducted by accumulating various experimental data in related fields in the future.

• Tuberculosis and Respiratory Diseases
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• v.86 no.3
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• pp.203-215
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• 2023

Background: Surgical resection is the standard treatment for early-stage lung cancer. Since postoperative lung function is related to mortality, predicted postoperative lung function is used to determine the treatment modality. The aim of this study was to evaluate the predictive performance of linear regression and machine learning models. Methods: We extracted data from the Clinical Data Warehouse and developed three sets: set I, the linear regression model; set II, machine learning models omitting the missing data: and set III, machine learning models imputing the missing data. Six machine learning models, the least absolute shrinkage and selection operator (LASSO), Ridge regression, ElasticNet, Random Forest, eXtreme gradient boosting (XGBoost), and the light gradient boosting machine (LightGBM) were implemented. The forced expiratory volume in 1 second measured 6 months after surgery was defined as the outcome. Five-fold cross-validation was performed for hyperparameter tuning of the machine learning models. The dataset was split into training and test datasets at a 70:30 ratio. Implementation was done after dataset splitting in set III. Predictive performance was evaluated by R² and mean squared error (MSE) in the three sets. Results: A total of 1,487 patients were included in sets I and III and 896 patients were included in set II. In set I, the R² value was 0.27 and in set II, LightGBM was the best model with the highest R² value of 0.5 and the lowest MSE of 154.95. In set III, LightGBM was the best model with the highest R² value of 0.56 and the lowest MSE of 174.07. Conclusion: The LightGBM model showed the best performance in predicting postoperative lung function.