• Journal of Wind Energy
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• v.2 no.1
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• pp.90-96
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• 2011

The purpose of this paper is to describe the control system for optimal performance of 2MW gearless PMSG wind turbine system, and to afford some techniques of the algorithm selection and design optimization of the wind turbine control system through analysis of load calculation and control characteristic. Wind turbine control system is composed of the main control system and remote control and monitoring system. The main control system is industrial PC based controller, and the remote control and monitoring system is a server based computer system. The main control system has a supervisory control of the wind turbine with operation procedures and power-speed control through the torque control by pitch angle. There are some applications to optimize the wind turbine system at the starting mode with increasing of rotor speed, and cut-in operating mode to prevent trundling cut-in and cut-out, a gain scheduling of pitch PID controller, torque scheduling and limitation of generation power by temperature limitation or remote command by remote control and monitoring system. Also, the server operation program of the remote control and monitoring system and the design of graphical display are described in this paper.

• Journal of Digital Convergence
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• v.17 no.3
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• pp.107-117
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• 2019

In the era of SNS, many people relies on it to express their emotions about various kinds of products and services. Therefore, for the companies eagerly seeking to investigate how their products and services are perceived in the market, emotion mining tasks using dataset from SNSs become important much more than ever. Basically, emotion mining is a branch of sentiment analysis which is based on BOW (bag-of-words) and TF-IDF. However, there are few studies on the emotion mining which adopt feature selection (FS) methods to look for optimal set of features ensuring better results. In this sense, this study aims to propose FS methods to conduct emotion mining tasks more effectively with better outcomes. This study uses Twitter and SemEval2007 dataset for the sake of emotion mining experiments. We applied three FS methods such as CFS (Correlation based FS), IG (Information Gain), and ReliefF. Emotion mining results were obtained from applying the selected features to nine classifiers. When applying DT (decision tree) to Tweet dataset, accuracy increases with CFS, IG, and ReliefF methods. When applying LR (logistic regression) to SemEval2007 dataset, accuracy increases with ReliefF method.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.31 no.6
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• pp.458-467
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• 2019

The purpose of this study is the suggestion of optimized parameters in OI (Optimal Interpolation) by experimental study. The observation of applying optimal interpolation is ADCP (Acoustic Doppler Current Profiler) data at the southwestern sea of Korea. FVCOM (Finite Volume Coastal Ocean Model) is used for the barotropic model. OI is to the estimation of the gain matrix by a minimum value between the background error covariance and the observation error covariance using the least square method. The scaling factor and correlation radius are very important parameters for OI. It is used to calculate the weight between observation data and model data in the model domain. The optimized parameters from the experiments were found by the Taylor diagram. Constantly each observation point requires optimizing each parameter for the best assimilation. Also, a high accuracy of numerical model means background error covariance is low and then it can decrease all of the parameters in OI. In conclusion, it is expected to have prepared the foundation for research for the selection of ocean observation points and the construction of ocean prediction systems in the future.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.1
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• pp.8-17
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• 2002

This paper suggests that the immune algorithm can effectively be used in tuning of a PID controller. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes according to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach have been used to describe dynamic model relationship between antibody and antigen. Therefore, there are some problems with a less flexible result to the external behavior. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. In addition to that, tuning performance cannot be guaranteed with regards to a plant with non-linear characteristics or many kinds of disturbances. Along with these, this paper used immune algorithm in order that a PID controller can be more adaptable controlled against the external condition, including moise or disturbance of plant. Parameters P, I, D encoded in antibody randomly are allocated during selection processes to obtain an optimal gain required for plant. The result of study shows the artificial immune can effectively be used to tune, since it can more fit modes or parameters of the PID controller than that of the conventional tuning methods.

• Asian-Australasian Journal of Animal Sciences
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• v.28 no.3
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• pp.451-455
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• 2015

The buffalo is an important livestock resource in several countries of South Asia and the Mediterranean regions. However, reproductive efficiency is compromised due to known problems of biological and management origins, such as lack of animal selection and poor nutrition. Under optimal conditions puberty is attained at 15 to 18 months in river buffalo, 21 to 24 months in swamp buffalo and is influenced by genotype, nutrition, management and climate. However, under field conditions these values deteriorate up to a significant extant. To improve reproductive efficiency, several protocols of oestrus and ovulation synchronization have been adopted from their use in commercial cattle production. These protocols yield encouraging pregnancy rates of (30% to 50%), which are comparable to those achieved in buffaloes bred at natural oestrus. The use of sexed semen in buffalo heifers also showed promising pregnancy rates (50%) when compared with conventional non-sexed semen. Assisted reproductive technologies have been transferred and adapted to buffalo but the efficiency of these technologies are low. However, these latest technologies offer the opportunity to accelerate the genetic gain in the buffalo industry after improving the technology and reducing its cost. Most buffaloes are kept under the small holder farming system in developing countries. Hence, future research should focus on simple, adoptable and impact-oriented approaches which identify the factors determining low fertility and oestrus behaviour in this species. Furthermore, role of kisspeptin needs to be explored in buffalo.

• The Journal of Korean Obstetrics and Gynecology
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• v.32 no.3
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• pp.1-19
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• 2019

Objectives: To select the optimal ranges showing obvious synergic anti-osteoporotic potential after adjust mixed formula consisted of Morindae Radix (MR) and Cistanchis Herba (CH) as compared with those of each single formula or risedronate sodium (RES) using bilateral ovariectomized (OVX) female mice. Methods: Fourteen groups, total eight sham or 104 OVX mice were selected based on the body weights at 34 days after OVX surgery. After that, 9 types mixed compositions, single formula of MR and CH, and RES were orally administered for 35 days. And we measured changes in body weight and gain, femur weight, bone mineral density (BMD), bone strength (failure load) and mineral content - calcium (Ca) and inorganic phosphorus (IP), osteocalcin contents and bone specific alkaline phosphatase (bALP) activities of all mice. Results: The OVX-induced estrogen-deficient osteoporotic signs were significantly inhibited by 35 days of continuous oral treatment of all treated mice as compared with OVX control mice. Especially, MR:CH 1:3 and 1:1 mixed formula treated mice showed significantly more favorable inhibitory activities against estrogen-deficient osteoporosis symptoms as compared to those of each single formula of MR and CH. Although RES also ameliorated the decreases of the femur BMD, strength and trabecular bone architectures through the inhibited the increases of bone turnover, but they did not critically influenced on the bone formations. Conclusions: The results suggest that MR:CH 1:3 mixed formula showed somewhat lower anti-resorptive effects as compared to those of RES, but they also showed bone formation effects. therefore, it is expected that MR:CH 1:3 mixture will be promising as a potent protective agents for relieving the osteoporosis in menopausal women.

• Information Systems Review
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• v.17 no.1
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• pp.49-64
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• 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 Transportation
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• v.17 no.5
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• pp.99-110
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• 1999

This study deals with the adaptability questions of O-D table estimation models. Its objectives are two-fold; (1) to estimate the characteristics of various O-D table estimation models(i.e. linear regression models. entropy models and statistic models) and (2) to find the model which estimates the O-D table with the best accuracy under the various data conditions. In Pursuing the above, this study gives the particular attentions to the test of the models, using the Sioux Falls network and equilibrium assignment method of MINUTP. The major findings are the followings. Firstly. it finds that the statistic models have the most goodness of fat among all models, if the required data are all Prepared. But it Presents that statistic models are the most sensitive against the underspecification and inconsistency problems of link data. Secondly, It shows that the linear regression models have the worst goodness of fat among all models. But the linear regression models are the most insensitive to the underspecification and inconsistency problems. Thirdly, THE/1 model of entropy model is sensitive against the underspecification and incon-sistency problems, but THE/2 model is insensitive. Finally, other informations like total volume, zonal Production and attraction volumes in 0-D table, help models to gain the better goodness of fit. Especially, in the statistic models. both the zonal production and attraction volume data are helpful to estimate the link volumes. It can be expected that the results dive some implications not only to the selection of optimal model under the various given data, but also to the development or modification of model.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.12
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• pp.1836-1843
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• 2019

Objective: Social genetic effects (SGE) are an important genetic component for growth, group productivity, and welfare in pigs. The present study was conducted to evaluate i) the feasibility of the single-step genomic best linear unbiased prediction (ssGBLUP) approach with the inclusion of SGE in the model in pigs, and ii) the changes in the contribution of heritable SGE to the phenotypic variance with different scaling ${\omega}$ constants for genomic relationships. Methods: The dataset included performance tested growth rate records (average daily gain) from 13,166 and 21,762 pigs Landrace (LR) and Yorkshire (YS), respectively. A total of 1,041 (LR) and 964 (YS) pigs were genotyped using the Illumina PorcineSNP60 v2 BeadChip panel. With the BLUPF90 software package, genetic parameters were estimated using a modified animal model for competitive traits. Giving a fixed weight to pedigree relationships (${\tau}:1$), several weights (${\omega}_{xx}$, 0.1 to 1.0; with a 0.1 interval) were scaled with the genomic relationship for best model fit with Akaike information criterion (AIC). Results: The genetic variances and total heritability estimates ($T^2$) were mostly higher with ssGBLUP than in the pedigree-based analysis. The model AIC value increased with any level of ${\omega}$ other than 0.6 and 0.5 in LR and YS, respectively, indicating the worse fit of those models. The theoretical accuracies of direct and social breeding value were increased by decreasing ${\omega}$ in both breeds, indicating the better accuracy of ${\omega}_{0.1}$ models. Therefore, the optimal values of ${\omega}$ to minimize AIC and to increase theoretical accuracy were 0.6 in LR and 0.5 in YS. Conclusion: In conclusion, single-step ssGBLUP model fitting SGE showed significant improvement in accuracy compared with the pedigree-based analysis method; therefore, it could be implemented in a pig population for genomic selection based on SGE, especially in South Korean populations, with appropriate further adjustment of tuning parameters for relationship matrices.

• Information Systems Review
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• v.20 no.4
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• pp.59-78
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• 2018

With the advent of the various device types and marketing communication, customer's search and purchase behavior have become more complex and segmented. However, extant research on multichannel marketing effects of the purchase funnel has not reflected the specific features of device User Interface (UI) and User Experience (UX). In this study, we analyzed the marketing channel effects of multi-device shoppers using a unique click stream dataset from global online retailers. We examined device types that activate online shopping and compared the differences between marketing channels that promote visits. In addition, we estimated the direct and indirect effects on visits and purchase revenue through customer's accumulated experience and channel conversions. The findings indicate that the same customer selects a different marketing channel according to the device selection. These results can help retailers gain a better understanding of customers' decision-making process in multi-marketing channel environment and devise the optimal strategy taking into account various device types. Our empirical analyses yield business implications based on the significant results from global big data analytics and contribute academically meaningful theoretical framework using an economic model. We also provide strategic insights attributed to the practical value of an online marketing manager.