• KIPS Transactions on Software and Data Engineering
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• v.5 no.1
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• pp.13-20
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• 2016

In this paper, we propose an object recognition system that can effectively find out its category, its instance name, and several attributes from the color and depth images of an object with hierarchical feature learning. In the preprocessing stage, our system transforms the depth images of the object into the surface normal vectors, which can represent the shape information of the object more precisely. In the feature learning stage, it extracts a set of patch features and image features from a pair of the color image and the surface normal vector through two-layered learning. And then the system trains a set of independent classification models with a set of labeled feature vectors and the SVM learning algorithm. Through experiments with UW RGB-D Object Dataset, we verify the performance of the proposed object recognition system.

• 유체기계공업학회:학술대회논문집
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• 2000.12a
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• pp.259-264
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• 2000

The functions of the plug valve are the control of flow rate as well closing and opening pipe lines. Analyses on the flow characteristics in plug valve port are required to improve the performance and safety at severe operating conditions such as high-pressure and high-temperature. In this study, numerical analyses are carried out with varying the opening rate (fraction of the full open to close) of the valve and the shapes of valve Uk: straight, convex, concave and mixed shapes. The parameters influencing the flow characteristics in the valve are the discharge coefficient( $C_v$) and the resistance coefficient( K). Therefore, the distributions of static pressure, velocity vector and stream lines are investigated, and $C_v$ and K are calculated in each opening rate and shape. In case of full open, the static pressure passed through the valve port has almost been recovered. However, in case of other opening rates, the pressure does not permanently regained due to pressure drop leading to loss. This phenomenon in each shape of the valve shows the different behaviors. Calculation results show that the mixed shape has the best flow attribute.

• East Asian Journal of Business Economics (EAJBE)
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• v.3 no.3
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• pp.14-26
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• 2015

A wind speed forecast is a crucial and sophisticated task in a wind farm for planning turbines and corresponds to an estimate of the expected production of one or more wind turbines in the near future. By production is often meant available power for wind farm considered (with units KW or MW depending on both the wind speed and direction. Such forecasts can also be expressed in terms of energy, by integrating power production over each time interval. In this study, we technically focused on mathematical modeling of wind speed and direction forecast based on locally data set gathered from Aghdasiyeh station in Tehran. The methodology is set on using most common techniques derived from literature review. Hence we applied the most sophisticated forecasting methods to embed seasonality, trend, and irregular pattern for wind speed as an angular variables. Through this research, we carried out the most common techniques such as the Box and Jenkins family, VARMA, the component method, the Weibull function and the Fourier series. Finally, the best fit for each forecasting method validated statistically based on white noise properties and the final comparisons using residual standard errors and mean absolute deviation from real data.

• Journal of KIISE:Software and Applications
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• v.29 no.9
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• pp.676-683
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• 2002

The success of XML(eXtensible Markup Language) is primarily based on its flexibility : everybody can define the structure of XML documents that represent information in the form he or she desires. XML is so flexible that XML documents cannot be automatically provided with an underlying semantics. Different tag sets, different names for elements or attributes, or different document structures in general mislead the task of classifying and clustering XML documents precisely. In this paper, we design and implement a system that allows checking the semantic-based similarity between XML tags. First, this system extracts the underlying semantics of tags and then expands the synonym set of tags using an WordNet thesaurus and user-defined word library which supports the abbreviation forms and compound words for XML tags. Seconds, considering the relative importance of XML tags in the XML documents, we extend a conventional vector space model which is the most generally used for document model in Information Retrieval field. Using this method, we have been able to check the similarity between XML tags which are represented different tags.

• Journal of the Korean Association of Geographic Information Studies
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• v.3 no.3
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• pp.69-76
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• 2000

This study deals with development GIS application using web-based CAD, this application serves to user, designer, manager that more convenient and various functions. Development to this application, collect attribute data from fieldwork and geographic data from cadastral map and aerial survey map and then development to user interface using HTML, JavaScript, ASP, Whip ActiveX control. This application's characters are as follows ; First, system designer designed that anyone who have basic knowledge about web and CAD can develop this application. A system structure simplification by 2-Tier. Geographic information use DWF(drawing web format) file and attribute information use DBMS in consideration of extension. Second, system manager can service independently GIS in Web need not high priced GIS engine, so more economical. Third, internet user get service GIS information and function that search of information, zoom in/out, pan, print etc., if you need more functions, add function without difficultly. Developed application as above, not only save volume but fast of speed as use vector data exclude character and image data. Also, this application can used by means of commercial and travel information service but also various GIS service of public institution and private in web.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.7-14
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• 2010

In this paper, we introduce a hierarchical internet application traffic classification system based on SVM as an alternative overcoming the uppermost limit of the conventional methodology which is using the port number or payload information. After selecting an optimal attribute subset of the bidirectional traffic flow data collected from the campus, the proposed system classifies the internet application traffic hierarchically. The system is composed of three layers: the first layer quickly determines P2P traffic and non-P2P traffic using a SVM, the second layer classifies P2P traffics into file-sharing, messenger, and TV, based on three SVDDs. The third layer makes specific classification of the entire 16 application traffics. By classifying the internet application traffic finely or coarsely, the proposed system can guarantee an efficient system resource management, a stable network environment, a seamless bandwidth, and an appropriate QoS. Also, even a new application traffic is added, it is possible to have a system incremental updating and scalability by training only a new SVDD without retraining the whole system. We validate the performance of our approach with computer experiments.

• Asian-Australasian Journal of Animal Sciences
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• v.32 no.7
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• pp.1015-1026
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• 2019

Objective: The objective of this study was to evaluate three different degrees of white striping (WS) addressing their automatic assessment and customer acceptance. The WS classification was performed based on a computer vision system (CVS), exploring different machine learning (ML) algorithms and the most important image features. Moreover, it was verified by consumer acceptance and purchase intent. Methods: The samples for image analysis were classified by trained specialists, according to severity degrees regarding visual and firmness aspects. Samples were obtained with a digital camera, and 25 features were extracted from these images. ML algorithms were applied aiming to induce a model capable of classifying the samples into three severity degrees. In addition, two sensory analyses were performed: 75 samples properly grilled were used for the first sensory test, and 9 photos for the second. All tests were performed using a 10-cm hybrid hedonic scale (acceptance test) and a 5-point scale (purchase intention). Results: The information gain metric ranked 13 attributes. However, just one type of image feature was not enough to describe the phenomenon. The classification models support vector machine, fuzzy-W, and random forest showed the best results with similar general accuracy (86.4%). The worst performance was obtained by multilayer perceptron (70.9%) with the high error rate in normal (NORM) sample predictions. The sensory analysis of acceptance verified that WS myopathy negatively affects the texture of the broiler breast fillets when grilled and the appearance attribute of the raw samples, which influenced the purchase intention scores of raw samples. Conclusion: The proposed system has proved to be adequate (fast and accurate) for the classification of WS samples. The sensory analysis of acceptance showed that WS myopathy negatively affects the tenderness of the broiler breast fillets when grilled, while the appearance attribute of the raw samples eventually influenced purchase intentions.

• The KIPS Transactions:PartD
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• v.10D no.3
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• pp.459-470
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• 2003

It is required to research on high-dimensional index structures for efficiently retrieving high-dimensional data because an attribute vector in data warehousing and a feature vector in multimedia database have a characteristic of high-dimensional data. For this, many high-dimensional index structures have been proposed, but they have so called ‘dimensional curse’ problem that retrieval performance is extremely decreased as the dimensionality is increased. To solve the problem, the cell-based filtering (CBF) scheme has been proposed. But the CBF scheme show a linear decreasing on performance as the dimensionality. To cope with the problem, it is necessary to make use of parallel processing techniques. In this paper, we propose a parallel CBF scheme which uses a horizontally-partitioned technique as declustering. In order to maximize the retrieval performance of the proposed parallel CBF scheme, we construct our parallel CBF scheme under a SN (Shared Nothing) cluster architecture. In addition, we present a data insertion algorithm, a rage query processing one, and a k-NN query processing one which are suitable for the SN cluster architecture. Finally, we show that our parallel CBF scheme achieves better retrieval performance in proportion to the number of servers in the SN cluster architecture, compared with the conventional CBF scheme.

• Korean journal of applied entomology
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• v.13 no.1 s.18
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• pp.25-31
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• 1974

This survey was aimed to accumulate basic data of aphid population at the Horticultural Experiment Station at Suweon. The yellow pan traps were setted at five locations (Fig.1.), and ran from May 1 to October 31. 1970. About one hundred and twenty species of aphids were trapped, including 24 species of plant vims vectors. Of these, dominant species were as follows: (Asterisk shows virus vector) Aphid species No. of catches * Aphis spiraecola PATCH 2,635, * Aphis craccivora KOCH 2,377, * Myzus persicae SULXER 2,111, Capitophorus hippophaes javanicus H.R. LAMBERS 2,051, Anoecia fulviabdominalis SASAKI 1,480, * Aphis gossypii GLOVER 867, * Macrosiphum avenae FABRICIUS 859, Cervaphis quercus TAKAHASHI 692, * Lipaphis erysimi KALTENBACH 645, Pleotrichophorus chrysanthemi THEOBALD 489, The above 10 species consisted $76.5\%$ of total catches and the 24 vector species consisted $55.5\%$. The curve of the seasonal occurrence of flying aphids at Horticultural Experiment Station shows bimodal, typical for the temperate region. The total number of trapped aphids at the Station from May to September, 1970, were less than that of average yearly catches at the College of Agriculture from 1967 to 1970. Thi, low numbers at Horticultural Experiment Station may attribute to the frequent spraying of insecticides from Spring to Summer on growing crops there. But the aphids population increase suddenly in the middle of October. This might be resulted from cease of insecticide applications and migration of aphids from summer host to winter host plants.

• Journal of Korea Society of Industrial Information Systems
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• v.17 no.7
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• pp.139-148
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• 2012

An ensemble of classifiers is to employ a set of individually trained classifiers and combine their predictions. It has been found that in most cases the ensembles produce more accurate predictions than the base classifiers. Combining outputs from multiple classifiers, known as ensemble learning, is one of the standard and most important techniques for improving classification accuracy in machine learning. An ensemble of classifiers is efficient only if the individual classifiers make decisions as diverse as possible. Bagging is the most popular method of ensemble learning to generate a diverse set of classifiers. Diversity in bagging is obtained by using different training sets. The different training data subsets are randomly drawn with replacement from the entire training dataset. The random subspace method is an ensemble construction technique using different attribute subsets. In the random subspace, the training dataset is also modified as in bagging. However, this modification is performed in the feature space. Bagging and random subspace are quite well known and popular ensemble algorithms. However, few studies have dealt with the integration of bagging and random subspace using SVM Classifiers, though there is a great potential for useful applications in this area. The focus of this paper is to propose methods for improving SVM performance using hybrid ensemble strategy for bankruptcy prediction. This paper applies the proposed ensemble model to the bankruptcy prediction problem using a real data set from Korean companies.