• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.2D
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• pp.153-161
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• 2010

The city planning of a local government is a continuous process that does not end with the creation of a plan but proceeds through decision-making, monitoring and evaluation phases. As a new city planning is changed and confirmed, there is a chance to construct a large scale road that is connected with an under constructed road. In this case, the expansion of the width and length of road, the addition of bridges or tunnels, and the change of the size and location of interchanges lead to many changes on road design and construction. In the past, the consignment contracts for a road construction have been made in limited numbers and for limited civil works. Now, it is growing in numbers and is making for large scale multi-works. However, the standard process and guidelines for the consignment contracts have not been suggested yet, so there is difficulty in performing the consigned road construction effectively. In this paper, the important factors for the consignment contracts are determined by construction document reviews and expert interviews. Based on these results, a standard process for the consigned contracts and a guideline for agreeing on construction cost are suggested. The costs that should be paid by a consignor are also defined.

• Journal of IKEEE
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• v.28 no.2
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• pp.150-157
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• 2024

In this paper, we propose a novel algorithm for predicting the number of apples on an apple tree using a deep learning-based object detection model and a polynomial regression model. Measuring the number of apples on an apple tree can be used to predict apple yield and to assess losses for determining agricultural disaster insurance payouts. To measure apple fruit load, we photographed the front and back sides of apple trees. We manually labeled the apples in the captured images to construct a dataset, which was then used to train a one-stage object detection CNN model. However, when apples on an apple tree are obscured by leaves, branches, or other parts of the tree, they may not be captured in images. Consequently, it becomes difficult for image recognition-based deep learning models to detect or infer the presence of these apples. To address this issue, we propose a two-stage inference process. In the first stage, we utilize an image-based deep learning model to count the number of apples in photos taken from both sides of the apple tree. In the second stage, we conduct a polynomial regression analysis, using the total apple count from the deep learning model as the independent variable, and the actual number of apples manually counted during an on-site visit to the orchard as the dependent variable. The performance evaluation of the two-stage inference system proposed in this paper showed an average accuracy of 90.98% in counting the number of apples on each apple tree. Therefore, the proposed method can significantly reduce the time and cost associated with manually counting apples. Furthermore, this approach has the potential to be widely adopted as a new foundational technology for fruit load estimation in related fields using deep learning.

• Journal of the Korea institute for structural maintenance and inspection
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• v.18 no.4
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• pp.50-58
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• 2014

Due to the cost burden of new construction, the necessity of repair and retrofitting of aged structures is sharply increasing as the domain of repair and retrofitting construction is expanding. Because of the necessity, new technologies for repair and retrofitting are continuously studied in Korea and foreign countries. Steel adhesive method, fiber reinforced plastic (FRP) surface adhesive method, and external prestressing method are used to perform the repair and retrofitting works in Korea. In order to consider a repair method using steel mesh reinforced cement mortar (SMCM), 3-point flexural member test was conducted considering repair area and layer number of SMCM. Five types of specimens including ordinary reinforced concrete (RC) specimen with dimensions of $1400{\times}500{\times}200$ (mm) were cast for testing the deflection measurement, a LVDT was installed at the top center of the specimens. Also, a steel strain gauge and a concrete strain gauge were placed at the center of the specimens. A steel strain gauge was also installed on the shear reinforcement. The 3 point flexural member test results showed that the maximum load of SMCM reinforced specimen was higher than that of basic RC specimen in all of the load-displacement curves. Also, the results showed that, when the whole lower part of the basic RC specimen was reinforced, the maximum load and strain were 1.18 and 1.37 times higher than that of the basic RC specimen, respectively. Each specimen showed a slightly different failure behavior where the difference of the results was caused by the difference in the adhesive level between SMCM and RC. Particularly, in SM-B1 specimen, SMCM spalled off during the experiment. This failure behavior showed that the adhesive performance for RC must be improved in order to utilize SMCM as repair and retrofitting material.

• Journal of Sericultural and Entomological Science
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• no.12
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• pp.47-50
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• 1970

There are many factors affecting the development of sericultural industry in Korea. The setting of a rational pricing system for sericultural products is one of important activities of the Korean Government to improve the incentives to producers. The determination o： the prices for many years were based on the production costs including a certain level of profits. Some of cost items are in conflict both in cocoon producers and silk-reeling industries. Government officials have to evaluate these conflicting problems and estimate the consequences of their decisions. In this situation the final decision often became political decisions. This analysis is aimed at providing an alternative method of setting the prices of sericultural products. The criteria of the equilibrium employed in this analysis are based on economic principle which equilibrium condition is determined by the relationships between the marginal productivity of input factors and factor prices. In order to obtain the related information Cobb-Douglas'functions were fitted using KIST computer and data were obtained mostly from the Bank of Korea and the Ministry of Agriculture and Forestru, An important assumption is that "Opportunity Costs" of factors input in both cocoon production and silk-Peeling industries are same, The major finding s obtained are as followings. 1) The sum of coefficient of production elastisity in silk-reeling industries is greater than one. Silk-reeling industries are operating under the situation of increasing return to scale and it is, therefore, expected to develop the industries as the capital-intensive large scale. 2) The cocoon producing farmers are under the situations of the decreasing return to scale and it is expected to continue their cocoon farming as the labor-intensive small scale, assuming the present level of production technology. As the development of commercial farming, the resources input in cocoon production will be shifted to the production for higher profitable crops, 3) The price elastisity of production is higher in cocoon production than in silk-reeling industries. It is expected that the price changing effects on domestic production will be resulted from cocoon producers. 4) Based on analysis results of marginal productivities and the opportunity costs of resources, cocoon price for meeting equilibrium price condition is to be increased by 8-16 percent or standard price level of silk increased by 6-8 percent. There were the possibilities of over evaluation on opportunity cost of resources input in silk-reeling industries, or income transfered from the farmers to the industries. It is recommended that the prices for meeting equilibrium price conditions are to be determined by 72 percent for cocoon and 28 percent for silk-reeling costs, based on standard level of the exporting prices.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of agriculture & life science
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• v.44 no.6
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• pp.91-99
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• 2010

We used a multiplex PCR primer set composed of 11 microsatellite (MS) markers and two sexing markers for gender detection. Genomic DNA extracted from hair roots of 3,510 Hanwoo were genotyped. Based on the 11MS markers, no animals had identical genotypes(TGLA227, BM2113, TGLA53, ETF10, SPS115, TGLA122, ETH3, ETH225, BM1824 and INRA23). The expected probability of identity among genotypes of random individuals (PI), the probability of identity among genotypes from random half-sibs ($PI_{half-sibs}$) and among genotypes of random individuals, and the probability of identity among genotypes from random sibs ($PI_{sibs}$) were estimated as $1.31{\times}10^{-23}$, $2.52{\times}10^{-16}$and $1.09{\times}10^{-6}$, respectively using the API-CALC program, version 1.0. We successfully completed the genotype analysis of 3,510 Hanwoo with a 3.93% genotyping failure rate. It was revealed that extracting DNA from the hair root was a time-efficient and cost-effective method to collect specimens for DNA isolation from live animals. This method also minimized stress for the animals during specimen collection. Among the hair roots from the back, belly, upper tail and lower tail, 5~13 hair roots of the lower tail led to the best genotype analysis results. Finally, we established a 96-well-format method of DNA preparation applicable for high- throughput genotype analysis.

• Journal of Biomedical Engineering Research
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• v.19 no.1
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• pp.59-68
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• 1998

Digital hearing aids offer many advantages over conventional analog hearing aids. With the advent of high speed digital signal processing chips, new digital techniques have been introduced to digital hearing aids. In addition, the evaluation of new ideas in hearing aids is necessarily accompanied by intensive subject-based clinical tests which requires much time and cost. In this paper, we present an objective method to evaluate and predict the performance of hearing aid systems without the help of such subject-based tests. In the hearing impairment simulation(HIS) algorithm, a sensorineural hearing impairment medel is established from auditory test data of the impaired subject being simulated. Also, the nonlinear behavior of the loudness recruitment is defined using hearing loss functions generated from the measurements. To transform the natural input sound into the impaired one, a frequency sampling filter is designed. The filter is continuously refreshed with the level-dependent frequency response function provided by the impairment model. To assess the performance, the HIS algorithm was implemented in real-time using a floating-point DSP. Signals processed with the real-time system were presented to normal subjects and their auditory data modified by the system was measured. The sensorineural hearing impairment was simulated and tested. The threshold of hearing and the speech discrimination tests exhibited the efficiency of the system in its use for the hearing impairment simulation. Using the HIS system we evaluated three typical hearing aid algorithms.

• The Korean Society of Law and Medicine
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• v.17 no.1
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• pp.299-346
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• 2016

There were also various decisions made in medical area in 2015. In the case that an inmate in a sanatorium was injured due to the reason which can be attributable to the sanatorium and the social welfare foundation that operates the sanatorium request treatment of the patient, the court set the standard of fixation of a party in medical contract. In the case that the family of the patient who was declared brain dead required withdrawal of meaningless life sustaining treatment but the hospital rejected and continued the treatment, the court made a decision regarding chargeable fee for such treatment. When it comes to the eye brightening operation which received measure of suspension from the Ministry of Health and Welfare for the first time in February, 2011, because of uncertainty of its safety, the court did not accept the illegality of such operation itself, however, ordered compensation of the whole damage based on the violation of liability for explanation, which is the omission of explanation about the fact that the cost-effectiveness is not sure as it is still in clinical test stage. There were numerous cases that courts actively acknowledged malpractices; in the cases of paresis syndrome after back surgery, quite a few malpractices during the surgery were acknowledged by the court and in the case of nosocomial infection, hospital's negligence to cause such nosocomial infection was acknowledged by the court. There was a decision which acknowledged malpractice by distinguishing the duty of installation of emergency equipment according to the Emergency Medical Service Act and duty of emergency measure in emergency situations, and a decision which acknowledged negligence of a hospital if the hospital did not take appropriate measures, although it was a very rare disease. In connection with the scope of compensation for damage, there were decisions which comply with substantive truth such as; a court applied different labor ability loss rate as the labor ability loss rate decreased after result of reappraisal of physical ability in appeal compared to the one in the first trial, and a court acknowledged lower labor ability loss rate than the result of appraisal of physical ability considering the condition of a patient, etc. In the event of any damage caused by malpractice, in regard to whether there is a limitation on liability in fee charge after such medical malpractice, the court rejected the hospital's claim for setoff saying that if the hospital only continued treatments to cure the patient or prevent aggravation of disease, the hospital cannot charge Medical bills to the patient. In regard to the provision of the Medical Law that prohibit medical advertisement which was not reviewed preliminarily and punish the violation of such, a decision of unconstitutionality was made as it is a precensorship by an administrative agency as the deliberative bodies such as Korean Medical Association, etc. cannot be denied to be considered as administrative bodies. When it comes to the issue whether PRP treatment, which is commonly performed clinically, should be considered as legally determined uninsured treatment, the court made it clear that legally determined uninsured treatment should not be decided by theoretical possibility or actual implementation but should be acknowledged its medical safety and effectiveness and included in medical care or legally determined uninsured treatment. Moreover, court acknowledged the illegality of investigation method or process in the administrative litigation regarding evaluation of suitability of sanatorium, however, denied the compensation liability or restitution of unjust enrichment of the Health Insurance Review & Assessment Service and the National Health Insurance Corporation as the evaluation agents did not cause such violation intentionally or negligently. We hope there will be more decisions which are closer to substantive truth through clear legal principles in respect of variously arisen issues in the future.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.23-46
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• 2017

Although there have been cases of evaluating the value of specific companies or projects which have centralized on developed countries in North America and Europe from the early 2000s, the system and methodology for estimating the economic value of individual technologies or patents has been activated on and on. Of course, there exist several online systems that qualitatively evaluate the technology's grade or the patent rating of the technology to be evaluated, as in 'KTRS' of the KIBO and 'SMART 3.1' of the Korea Invention Promotion Association. However, a web-based technology valuation system, referred to as 'STAR-Value system' that calculates the quantitative values of the subject technology for various purposes such as business feasibility analysis, investment attraction, tax/litigation, etc., has been officially opened and recently spreading. In this study, we introduce the type of methodology and evaluation model, reference information supporting these theories, and how database associated are utilized, focusing various modules and frameworks embedded in STAR-Value system. In particular, there are six valuation methods, including the discounted cash flow method (DCF), which is a representative one based on the income approach that anticipates future economic income to be valued at present, and the relief-from-royalty method, which calculates the present value of royalties' where we consider the contribution of the subject technology towards the business value created as the royalty rate. We look at how models and related support information (technology life, corporate (business) financial information, discount rate, industrial technology factors, etc.) can be used and linked in a intelligent manner. Based on the classification of information such as International Patent Classification (IPC) or Korea Standard Industry Classification (KSIC) for technology to be evaluated, the STAR-Value system automatically returns meta data such as technology cycle time (TCT), sales growth rate and profitability data of similar company or industry sector, weighted average cost of capital (WACC), indices of industrial technology factors, etc., and apply adjustment factors to them, so that the result of technology value calculation has high reliability and objectivity. Furthermore, if the information on the potential market size of the target technology and the market share of the commercialization subject refers to data-driven information, or if the estimated value range of similar technologies by industry sector is provided from the evaluation cases which are already completed and accumulated in database, the STAR-Value is anticipated that it will enable to present highly accurate value range in real time by intelligently linking various support modules. Including the explanation of the various valuation models and relevant primary variables as presented in this paper, the STAR-Value system intends to utilize more systematically and in a data-driven way by supporting the optimal model selection guideline module, intelligent technology value range reasoning module, and similar company selection based market share prediction module, etc. In addition, the research on the development and intelligence of the web-based STAR-Value system is significant in that it widely spread the web-based system that can be used in the validation and application to practices of the theoretical feasibility of the technology valuation field, and it is expected that it could be utilized in various fields of technology commercialization.

• Asia pacific journal of information systems
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• v.19 no.2
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• pp.157-178
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• 2009

Corporate credit rating is a very important factor in the market for corporate debt. Information concerning corporate operations is often disseminated to market participants through the changes in credit ratings that are published by professional rating agencies, such as Standard and Poor's (S&P) and Moody's Investor Service. Since these agencies generally require a large fee for the service, and the periodically provided ratings sometimes do not reflect the default risk of the company at the time, it may be advantageous for bond-market participants to be able to classify credit ratings before the agencies actually publish them. As a result, it is very important for companies (especially, financial companies) to develop a proper model of credit rating. From a technical perspective, the credit rating constitutes a typical, multiclass, classification problem because rating agencies generally have ten or more categories of ratings. For example, S&P's ratings range from AAA for the highest-quality bonds to D for the lowest-quality bonds. The professional rating agencies emphasize the importance of analysts' subjective judgments in the determination of credit ratings. However, in practice, a mathematical model that uses the financial variables of companies plays an important role in determining credit ratings, since it is convenient to apply and cost efficient. These financial variables include the ratios that represent a company's leverage status, liquidity status, and profitability status. Several statistical and artificial intelligence (AI) techniques have been applied as tools for predicting credit ratings. Among them, artificial neural networks are most prevalent in the area of finance because of their broad applicability to many business problems and their preeminent ability to adapt. However, artificial neural networks also have many defects, including the difficulty in determining the values of the control parameters and the number of processing elements in the layer as well as the risk of over-fitting. Of late, because of their robustness and high accuracy, support vector machines (SVMs) have become popular as a solution for problems with generating accurate prediction. An SVM's solution may be globally optimal because SVMs seek to minimize structural risk. On the other hand, artificial neural network models may tend to find locally optimal solutions because they seek to minimize empirical risk. In addition, no parameters need to be tuned in SVMs, barring the upper bound for non-separable cases in linear SVMs. Since SVMs were originally devised for binary classification, however they are not intrinsically geared for multiclass classifications as in credit ratings. Thus, researchers have tried to extend the original SVM to multiclass classification. Hitherto, a variety of techniques to extend standard SVMs to multiclass SVMs (MSVMs) has been proposed in the literature Only a few types of MSVM are, however, tested using prior studies that apply MSVMs to credit ratings studies. In this study, we examined six different techniques of MSVMs: (1) One-Against-One, (2) One-Against-AIL (3) DAGSVM, (4) ECOC, (5) Method of Weston and Watkins, and (6) Method of Crammer and Singer. In addition, we examined the prediction accuracy of some modified version of conventional MSVM techniques. To find the most appropriate technique of MSVMs for corporate bond rating, we applied all the techniques of MSVMs to a real-world case of credit rating in Korea. The best application is in corporate bond rating, which is the most frequently studied area of credit rating for specific debt issues or other financial obligations. For our study the research data were collected from National Information and Credit Evaluation, Inc., a major bond-rating company in Korea. The data set is comprised of the bond-ratings for the year 2002 and various financial variables for 1,295 companies from the manufacturing industry in Korea. We compared the results of these techniques with one another, and with those of traditional methods for credit ratings, such as multiple discriminant analysis (MDA), multinomial logistic regression (MLOGIT), and artificial neural networks (ANNs). As a result, we found that DAGSVM with an ordered list was the best approach for the prediction of bond rating. In addition, we found that the modified version of ECOC approach can yield higher prediction accuracy for the cases showing clear patterns.