• Resources Recycling
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• v.31 no.6
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• pp.66-72
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• 2022

The circularity and particle size distribution of products obtained from dry classification after circularity tests using a high-intensity mixer were investigated to evaluate the use of domestic graphite concentrate as a lithium-ion battery material. At a rotor speed of 3,000 rpm, the particle size and circularity of the concentrated sample and product were unchanged. The circularity increased and particle size decreased when the rotor speeds were increased to 6,000 rpm, 10,000 rpm, and 12,000 rpm and the operating time was increased. For instance, the circularity increased from 0.870 to 0.936 when the rotor speed was increased from 3,000 rpm to 12,000 rpm for an operating time of 10 min. After the circularity test, dry classification was performed, wherein the circularity of the coarse particles was found to have increased to 0.947. Round particles were observed in the SEM images, indicating that high circularity was successfully achieved.

• Journal of the Korean Home Economics Association
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• v.49 no.5
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• pp.1-10
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• 2011

To provide the basic data to manufacture superior brassiere, this study was analyzed the evaluation of wear sensation of brassiere and the satisfaction of breast type by breast circumference and volume of 182 twenties-aged women. The results were as follows: First, it was reliable to set up the cup size of brassiere by using the breast circumference. Hemispherical breast was the same as cone-shaped breast in breast classification by breast circumference and volume. Second, the breast sizes were able to classify into under 200cc, 200~300cc, 300~400cc, and over 400cc by volume, but measuring the volume was more difficult than measuring the breast circumference. Last, there were correlations between breast circumference and breast volume by breast type. And there were differences on improvement, brassiere size, and the satisfaction of breast type by breast circumference and volume. This results will give basic informations for brassiere design that reflects breast type according to breast circumference and volume for functional brassiere.

• Journal of ILASS-Korea
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• v.10 no.2
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• pp.18-31
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• 2005

This study was performed to explore the liquid breakup and atomization characteristics for the classification of drop formation mode and background of uniform droplets generation in electrohydrodynmaic atomization according to the change of experimental parameters such as nozzle material (stainless steel. teflon). fluid flow rate, applied electrical field and intensity, and frequency. In results, from the classification map of drop formation modes according to the variation of applied AC voltage and frequency at a stainless nozzle, the droplet size was smaller than the outer diameter of the nozzle tip relatively in the spindle mode. The transition points became clearly to be moved toward the high applied voltage by rising the applied AC frequency beyond 450Hz. Also the droplet radius can be observed quite small in the frequency bandwidth of $350{\sim}450Hz$. The droplet radiuses decrease as the applied voltage increases for a fixed applied AC frequency within the range from 50Hz to 400Hz Over 400Hz, the relation between the power intensity and the droplet size was not consistent with a continuous mechanism of liquid breakup. Thus, it is showed that the droplet size distribution using the teflon nozzle was analogous to the results of stainless steel, but the droplet size was bigger than that of stainless steel relatively in case of a teflon nozzle.

• Nuclear Engineering and Technology
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• v.54 no.1
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• pp.193-206
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• 2022

Several algorithms for nuclear power plant (NPP) break event detection, isolation, localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the noise subspace of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of deep neural networks in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks' sizes.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.65-74
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• 2022

In natural language processing, commonly used words such as prepositions are referred to as stop-words; they have no inherent meaning and are therefore ignored in indexing and retrieval tasks. The removal of stop-words from Arabic text has a significant impact in terms of reducing the size of a cor- pus text, which leads to an improvement in the effectiveness and performance of Arabic-language processing systems. This study investigated the effectiveness of applying a stop-word lists elimination with normalization as a preprocessing step. The idea was to merge statistical method with the linguistic method to attain the best efficacy, and comparing the effects of this two-pronged approach in reducing corpus size for Ara- bic natural language processing systems. Three stop-word lists were considered: an Arabic Text Lookup Stop-list, Frequency- based Stop-list using Zipf's law, and Combined Stop-list. An experiment was conducted using a selected file from the Arabic Newswire data set. In the experiment, the size of the cor- pus was compared after removing the words contained in each list. The results showed that the best reduction in size was achieved by using the Combined Stop-list with normalization, with a word count reduction of 452930 and a compression rate of 30%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1348-1375
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• 2018

Support Vector Machine (SVM) is a well-known machine learning classification algorithm, which has been widely applied to many data mining problems, with good accuracy. However, SVM classification speed decreases with increase in dataset size. Some applications, like video surveillance and intrusion detection, requires a classifier to be trained very quickly, and on large datasets. Hence, this paper introduces two filter-based instance selection techniques for optimizing SVM training speed. Fast classification is often achieved at the expense of classification accuracy, and some applications, such as phishing and spam email classifiers, are very sensitive to slight drop in classification accuracy. Hence, this paper also introduces two wrapper-based instance selection techniques for improving SVM predictive accuracy and training speed. The wrapper and filter based techniques are inspired by Cuckoo Search Algorithm and Bat Algorithm. The proposed techniques are validated on three popular e-fraud types: credit card fraud, spam email and phishing email. In addition, the proposed techniques are validated on 20 other datasets provided by UCI data repository. Moreover, statistical analysis is performed and experimental results reveals that the filter-based and wrapper-based techniques significantly improved SVM classification speed. Also, results reveal that the wrapper-based techniques improved SVM predictive accuracy in most cases.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.3
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• pp.591-609
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• 2024

In this study, preprocessings with all combinations were examined in terms of the effects on decreasing word number, shortening the duration of the process and the classification success in balanced and imbalanced datasets which were unbalanced in different ratios. The decreases in the word number and the processing time provided by preprocessings were interrelated. It was seen that more successful classifications were made with Turkish datasets and English datasets were affected more from the situation of whether the dataset is balanced or not. It was found out that the incorrect classifications, which are in the classes having few documents in highly imbalanced datasets, were made by assigning to the class close to the related class in terms of topic in Turkish datasets and to the class which have many documents in English datasets. In terms of average scores, the highest classification was obtained in Turkish datasets as follows: with not applying lowercase, applying stemming and removing stop words, and in English datasets as follows: with applying lowercase and stemming, removing stop words. Applying stemming was the most important preprocessing method which increases the success in Turkish datasets, whereas removing stop words in English datasets. The maximum scores revealed that feature selection, feature size and classifier are more effective than preprocessing in classification success. It was concluded that preprocessing is necessary for text classification because it shortens the processing time and can achieve high classification success, a preprocessing method does not have the same effect in all languages, and different preprocessing methods are more successful for different languages.

• Journal of the Korean Society for information Management
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• v.35 no.2
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• pp.37-62
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• 2018

This study examined the factors affecting the performance of automatic classification based on machine learning for domestic journal articles in the field of LIS. In particular, In view of the classification performance that assigning automatically the class labels to the articles in "Journal of the Korean Society for Information Management", I investigated the characteristics of the key factors(weighting schemes, training set size, classification algorithms, label assigning methods) through the diversified experiments. Consequently, It is effective to apply each element appropriately according to the classification environment and the characteristics of the document set, and a fairly good performance can be obtained by using a simpler model. In addition, the classification of domestic journals can be considered as a multi-label classification that assigns more than one category to a specific article. Therefore, I proposed an optimal classification model using simple and fast classification algorithm and small learning set considering this environment.

• Journal of Korea Technology Innovation Society
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• v.18 no.3
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• pp.387-415
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• 2015

In this study, we have proposed an estimation model of domestic market size using the linking between standard statistical classification systems, and reviewed the practical applicability of the model. The results of the mining and manufacturing survey of Statistics Korea conducted on the basis of KSIC (Korea Standard Industrial Classification) and Korea trade statistics based on HS (The Harmonized Commodity Description and Coding System; Harmonized System) classification were linked for the model by using the correspondence tables provided by Statistics Korea and United Nations Statistics Division. The most serious problem to adopt the integrated KSIC-ISIC-HS correspondence table for the estimation of domestic market size is the complex multiple linkages among KSIC and HS codes. In this study, we have suggested the method to divide the amount of trade corresponding to the HS codes linked to more than two ISIC codes based on the ratio of shipments corresponding to the ISIC codes as the weight. Then, it is possible to analyze the domestic market size of 125 ISIC codes in the manufacturing industry and to forecast the market size in the near future by using the model. Although the model has some limitations such as the difficulty in analysis on more subdivided items than ISIC items, the impossibility of the analysis on items in industries except for manufacturing, errors in the shipment due to some missing data, this study has significance in the sense that it provided the analysis method of domestic market size by using the most objective, reliable and sustainably useful data.

• Journal of Korean Society of Transportation
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• v.17 no.4
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• pp.111-124
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• 1999

The Purpose of this thesis is to develop a vehicle classification algorithm using single Magnetometer detector during presence time of vehicle detection and is to examine a held application from field test. We collected data using Magnetometer detector on freeway and used digital data to change voltage values according to magnetic flux density in analysis. We collected these datum during the presence time and then obtained characteristics from wave form in these datum. Based on these characteristics, We used the following three methods for this a1gorithm :1. Template Matching Method,2. Neural Network Method using Back-propagation Algorithm 3. Complex Method using changed slope points and mixing method 1, 2. Of course, Before processing of over three methods, These data were processed normalizing by 20, 40 of size in only X axis and moving average by 0, 3, 4, 5 of size. Vehicle classification were Processed in three steps ; 2, 3, 5 types classification. In 2 types vehicle classification, recognition rate is 83% by template matching method.