• The Journal of Fisheries Business Administration
• /
• v.53 no.1
• /
• pp.1-16
• /
• 2022

This paper aims to propose a new systematic approach to analyze the factor productivity and to investigate those characteristics of factor productivity in operational and managerial perspectives. The Cobb-Douglas production function is adopted to estimate the labor and capital productivity. In estimating those productivities the data of The Research on the Actual Condition of Coastal Fisheries (RACF), especially those of Jeon-Nam Province are used. The statistical analysis of RACF data shows that the characteristics are a little bit different between labor and capital of the operational equipment in the coastal fisheries. The Cobb-Douglas type production function is useful in estimating the factor productivity, especially in case of 'coastal Stow-net fishery' even though the limited data is used. However, in case of 'trap fishery,' the Cobb-Douglas production function appears to have some limitations in estimation. This implies that estimating the factor productivities in fisheries employing broad perspectives and various methods are needed.

• Korean Journal of Food Science and Technology
• /
• v.42 no.6
• /
• pp.699-704
• /
• 2010

Rapeseed oil was stored under different conditions such as in the dark, with UV treatment, and with prooxidantscytochrome C and copper ion. The rapeseed oils stored at different temperatures were analyzed by a mass spectrometrybased electronic nose and discriminant function analysis (DFA). Volatile components in the rapeseed oil increased with storage time, and the discriminant function first score (DF1) moved from a positive position to a negative position as storage time increased. Changes in DF1 were higher under UV treatment than under the dark condition (DF1: $r^2$=0.9481, F=307.03). The different DF1 values (F1) under the dark condition were 0.099, 0.187, and 0.278 as storage temperature increased. The different values under UV treatment were 0.554, 0.588, and 0.542, as storage temperature increased from 4 to $26^{\circ}C$. As concentrations of prooxidants copper ion and cytochrome C increased, amounts of volatile components also increased. These were confirmed by DFA. Furthermore, changes in responses at each ion fragment agreed with reported results for GC/MS, which formed after rancidity of the oil, including pentane, pentanal, 1-pentanol, hexanal, n-octane, 2-hexenal, heptanal, 2-heptenal, decane, 2-octenal, undecane, and dodecane.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.61 no.5
• /
• pp.744-752
• /
• 2012

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as an one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problems. In data preprocessing part, Principal Component Analysis(PCA) which is generally used in face recognition, which is useful to express some classes using reduction, since it is effective to maintain the rate of recognition and to reduce the amount of data at the same time. However, because of there of the whole face image, it can not guarantee the detection rate about the change of viewpoint and whole image. Thus, to compensate for the defects, Linear Discriminant Analysis(LDA) is used to enhance the separation of different classes. In this paper, we combine the PCA&LDA algorithm and design the optimized pRBFNNs for recognition module. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in 'If-then' format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as two kinds of polynomials such as constant, and linear. The coefficients of connection weight identified with back-propagation using gradient descent method. The output of the pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of Differential Evolution. The proposed pRBFNNs are applied to face image(ex Yale, AT&T) datasets and then demonstrated from the viewpoint of the output performance and recognition rate.

• Proceedings of the KOSOMBE Conference
• /
• v.1992 no.05
• /
• pp.155-160
• /
• 1992

Liver function test is one of the most common tests for diagnosis and follow-up of patients and for heal th screening. Automatic interpretation and suggestions on the diagnostic possibilities contribute to shorten the interpretation time of the test results and help to provide qualified health care. Fuzzy logic has been recently introduced and being spread for these purposes. The present study aims at model Ins the foray rule-based laboratory diagnosis system. The fuzzy rule-based laboratory diagnosis system was applied to the diagnosis regarding liver function test. The system was evaluated by comparing with the stepwise multivariate discriminant function analysis, which showed similar results, and the overall accuracy of the fuzzy diagnosis system was about 80%.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2002.05a
• /
• pp.1044-1051
• /
• 2002

This paper proposes a two-phase mathematical programming approach by considering classification gap to solve the proposed credit scoring problem so as to complement any theoretical shortcomings. Specifically, by using the linear programming (LP) approach, phase 1 is to make the associated decisions such as issuing grant of credit or denial of credit to applicants. or to seek any additional information before making the final decision. Phase 2 is to find a cut-off value, which minimizes any misclassification penalty (cost) to be incurred due to granting credit to 'bad' loan applicant or denying credit to 'good' loan applicant by using the mixed-integer programming (MIP) approach. This approach is expected to and appropriate classification scores and a cut-off value with respect to deviation and misclassification cost, respectively. Statistical discriminant analysis methods have been commonly considered to deal with classification problems for credit scoring. In recent years, much theoretical research has focused on the application of mathematical programming techniques to the discriminant problems. It has been reported that mathematical programming techniques could outperform statistical discriminant techniques in some applications, while mathematical programming techniques may suffer from some theoretical shortcomings. The performance of the proposed two-phase approach is evaluated in this paper with line data and loan applicants data, by comparing with three other approaches including Fisher's linear discriminant function, logistic regression and some other existing mathematical programming approaches, which are considered as the performance benchmarks. The evaluation results show that the proposed two-phase mathematical programming approach outperforms the aforementioned statistical approaches. In some cases, two-phase mathematical programming approach marginally outperforms both the statistical approaches and the other existing mathematical programming approaches.

• Food Science of Animal Resources
• /
• v.32 no.5
• /
• pp.669-677
• /
• 2012

The objective of this study was to analyze the flavor pattern of different varieties of cheeses. Four of the each following cheese varieties such as shred type pizza cheese, Cheddar cheese, Mozzarella block cheese, and white mold-ripened cheeses, stored at $4^{\circ}C$ during 2 wks were examined before and after cooking at $70^{\circ}C$ and $160^{\circ}C$. Flavor patterns of these cheeses were analyzed using an electronic nose system based on mass spectrometer. All data were treated by multivariate data processing based on discriminant function analysis (DFA). The results showed the discriminant model by DFA method. Data revealed that flavor patterns of pizza cheeses were well separated as storage prolonged and obviously discriminated as the higher the cooking temperature. The result of pattern recognition analysis based on discriminant function analysis showed that new brand of pizza cheese produced by Imsil Cheese Cooperative was located at middle between the flavors of the imported brands of pizza cheese and those of domestic brand of pizza cheeses. Imsil cheese has a unique flavor pattern among other variety of cheeses. Application of pattern recognition analysis by electronic nose might be useful and advanced technology for characterizing in flavor pattern of cheese products from different origins and different categories of cheeses.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2017.04a
• /
• pp.23-23
• /
• 2017

Recent favor on high quality food and concern on food safety have demonstrated the superiority of Hanwoo (Korean native cattle). In general, the price of cow is higher than those of steer and bull, causing cheating issues in the market. Hence, this study is to discriminate genders of Hanwoo with identification of factors which highly influence gender discrimination based on the big-size deboning data. Totally, there were 31 variables in the deboning data, and we divided into them two categories: data obtained before and after deboning. Discriminant function analysis was then applied into the data to determined the accuracy of gender discrimination in Hanwoo. The result showed that Hanwoo could be classified by gender with 99.2% of accuracy when using all 31 variables. In detail, it was possible to identify 93 of 94 bulls (98.9%), 96 of 96 cows (100%) and 74 of 75 steers (98.7%). The most significant variables was chuck, sirloin, armbone shin, plates, retail and cuts percentage, sequentially. With variables obtainable before deboning, accuracies of classification were 91.5% for bulls, 92.7% for cows, and 89.3% for steers. The most significant variables was water, cold carcass weight and back-fat thickness. The discrimination accuracy was higher with data obtainable after deboning: bulls (98.9%), cows (99.0%) and steers (98.7%). In this case, chuck, sirloin and armbone shin were the factors determined the classification ability. This study showed that Hanwoo can be classified based on deboning data with appropriate statistics, further suggesting weight of cut of beef might be the standard for gender classification.

• Korean Journal of Social Welfare
• /
• v.50
• /
• pp.157-172
• /
• 2002

Most of existing studies about employment of people with disabilities have regarded the disabled groups as only one group which has different characteristics respectively. In fact, each of the disabled groups have several peculiarities of their own. So government or policy makers must regard the disabled group as different groups which have their own characteristics(example : education level, house income, wage, working hours, satisfaction level for their job, disability type and degree, service year in a firm, etc) for promoting employment of the disabled effectively and keeping their job continuously. In this study, we examine and verify the existence of segmentations in Korea labor market of the disabled according to their different education level & sex by using MDFA(Multiple Discriminant Function Analysis technique) and seek solutions for easing those segmentations.

• Journal of the Korean Data and Information Science Society
• /
• v.22 no.5
• /
• pp.931-940
• /
• 2011

Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.9 no.5
• /
• pp.986-994
• /
• 2005

This Paper proposes the likelihood-based nonlinear dimension reduction method of the speech feature parameters in order to construct the voice activity detecter adaptable in noisy environment. The proposed method uses the nonlinear values of the Gaussian probability density function with the new parameters for the speec/nonspeech class. We adapted Likelihood Ratio Test to find speech part and compared its performance with that of Linear Discriminant Analysis technique. In experiments we found that the proposed method has the similar results to that of Gaussian Mixture Models.