• Journal of Korea Multimedia Society
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• v.21 no.6
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• pp.659-666
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• 2018

The classification of the tablets recovered according to prescription changes is usually carried out manually by a number of pharmacists at the hospitals. Relatively high-wage pharmacists carry out the reclassification of the tablets, which results in a large loss of time and labor, and if the tablets are incorrectly classified, this can lead to medical accidents. In order to overcome these problems, a new automatic tablet classifying machine has been introduced. In the conventional automatic tablet classifying machine, tablets having various shapes, sizes, and colors are transferred to a classifying machine through the line feeder. Problems such as breakaway of the tablets from the line feeder, pilling of the tablets in the line feeder, and appearance contamination of the tablets occur. In this paper, we propose a system that automatically classifies the shape, size, and color of tablets through individual supply method by vacuum adsorption and image processing.

• Journal of the Korean Society for Library and Information Science
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• v.25
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• pp.185-213
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• 1993

This study is on the history of classification in Western library from ancient to Harris(1870), before Dewey. It looks into the classification systems of librarians, bibliographers, booksellers and libraries of that time. One of the earliest was the classification of the clay tablets in the Assyrian library of Assurbanipal. But the earliest recorded system in the papyrus is that which Callimachus(B.C. 310-240) devised for the library at Alexandria. In the medival, the monastry libraries used many classifications. but their libraries were very small. Gesner, Naude, Brunet, Jefferson, Edwards, Harris etc. tried to make a good classification for bibliographies and libraries. Especially Brunet made the scheme based on the French system, and it used on bibliographical classification and shelf classification in the many libraries. In 1859, Edwards made the classification scheme for the public library in the Great Britain. In 1870, Harris made the famous inverted Baconian classification and it strongly influenced the Dewey Decimal Classification.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.8
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• pp.628-635
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• 2017

It is important to prescribe and take medicines that are appropriate for symptoms, since medicines are closely related to human health and life. Moreover, it becomes more important to accurately classify genuine medicines with counterfeit, since the number of counterfeit increases worldwide. However, the number of high-quality experts who have enough experience to properly classify them is limited and there exists a need for the automatic technique to classify medicine tablets. In this paper, we propose a method to classify the tablets by using a handheld spectrometer which provides both Near Infra-Red (NIR) and visible light spectrums. We adopted Support Vector Machine(SVM) as a machine learning algorithm for tablet classification. As a result of the simulation, we could obtain the classification accuracy of 99.9 % on average by using both NIR and visible light spectrums. Also, we proposed a two-step SVM approach to discriminate the counterfeit tablets from the genuine ones. This method could improve both the accuracy and the processing time.

• YAKHAK HOEJI
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• v.44 no.5
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• pp.424-431
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• 2000

A biopharmaceutics drug classification system for correlation between in vitro dissolution and in vivo bioavailability is proposed based on recognizing that drug dissolution and gastrointestinal permeability are the fundamental parameters controlling the rate and extent of drug absorption. The objective of this study was to assess whether in vitro dissolution profiles of immediate-release beta-blocker tablets can be correlated with intestinal membrane permeability and/or in vivo bioavailability In vitro dissolution of the beta-blocker tablets was examined using KP VII Apparatus II methods at various pH. Intestinal membrane permeability was determined in vitro using the diffusion chamber method. Bioavailablity parameters were cited from literatures. The dissolution profiles did not accurately represent the in vivo bioavailablity However there were good correlations between intestinal membrane permeability and log P (noctanol/buffer). The correlations obtained in this study indicated that in vitro diffusion chamber method could be used to predict intestinal absorption in vivo.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.49-56
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• 2014

This thesis proposes CBIRS/TB method that uses a tablet's color distribution information and form distinctive in content-based search. CBIRS/TB can avoid misuses and improper tablet uses by conducting content-based search in commonly prescribed tablets. The existing FE-CBIRS system is limited to recognizing only the image of color and shape of the tablet, that leads to applying insufficient form-specific information. While CBIRS/TB utilizes average, standard deviation, hue and saturation of each tablets in color, brightness, and contrast, FE-CBIRS has partial-sphere application problem; only applying the typical color of the tablet. Also, in case of the shape-specific-information, Invariant Moment is mainly used for the extracted partial-spheres. This causes delayed processing time and accuracy problems. Therefore, to improve this setback, this thesis indexed color-specific-information of the extracted images into categorized classification for improved search speed and accuracy.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.6 no.1
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• pp.47-80
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• 1984

The aim of this paper is to study the classification and arrangement system, the original texts, and the bibliographic deseription of Dae-Jang-Mock-Lock. The results of this study can be summarized as followings; 1. The first carving of Dae-Jang-Mock-Lock was assumed to be completed at latest by 1087 (King Sun-Jong 4, Koryo) when the first carving of Dae-Jang-Kyong, the complete collection of Buddhist Sutra, mas finished. Henee, Dae-Jang-Mock-Lack is safely said to be the oldest catalog in Korea. 2. The major function of Dae-Jang-Mock-Lock mas to facilitate the job of printing, managing, or arranging the Sutra tablets, rather than to serve as its references. 3. Dae-Jang-Mock-Lock mas classified in accordance with the classification sys tern of Gae-Woon-Suck-Kyo-Lock, a chinese Buddhist catalog. This system classified the complete collection of Buddhist Sutra into the three categories of "Mah$\={a}$y$\={a}$na Tripitaka", "Hinayan$\={a}$ Tripitaha", and "collected Biographies of Samgha", at the first gradation. And then the Mah$\={a}$yan$\={a}$ Tripitaka mas divided into the three categories of "Mah$\={a}$y$\={a}$na Sutra", "Mah$\={a}$y$\={a}$na Uparaksa", and "Mah$\={a}$y$\={a}$na Upadesa", at the second gradation. In the same manner the "Hinayan$\={a}$ Tripitaka was divided into "Hinayan$\={a}$ Sutra", "Hinayan$\={a}$ Uparaksa", and "Hinayan$\={a}$ Upadesa". The "Collected Biographies of Samgha" was divided into Brahman Samgha and Chinese Samgha. For this reason we Can name this main classification system as a Tripitaka Classification. 4. The first carving tablets of the Buddhist Sutra from Choen Shelf (天函) to Young Shelf (英函) were the same Sutra that were included in Gae-Woen-Suck-Kyo-Lock (開元釋敎錄), except those 4 omitted sutras of 22 volumes. But the other 7 sutras of 24 volumes were included as an extra addition in "Dae-Jang-Mock-Lock." 5. The 40 shelves and 376 volumes of Buddhist Sutra from the Doo Shelf (杜函) to the Kyong Shelf (輕函) in Dae-Jang-Mock-Lock were copied from the texts of Guran Edition (契丹本) 6. The 36 shelves of Buddhist Sutras from the Bun shelf (磻函) to the Mil shelf (密函) in Dae-Jang-Mock-Lock were the same as those included in Sock-Jung-Woen-Suck-Kyo-Lock (續貞元釋敎錄), except the 3 omitted sutras.

• Proceedings of the Korean Society of Near Infrared Spectroscopy Conference
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• 2001.06a
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• pp.3112-3112
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• 2001

Near-Infrared Spectroscopy (NIRS) was investigated aiming at the identification of falsified drugs. The identification is based on comparison of the NIR spectrum of a sample with a typical spectra of an authentic drug using multivariate modelling and classification algorithms (PCA/SIMCA). Two spectrophotometers (Brimrose - Luminar 2000 and 2030), based on acoustic-optical filter (AOTF) technology, sharing the same controlling computer, software (Brimrose - Snap 2.03) and the data acquisition electronics, were employed. The Luminar 2000 scans the range 850 1800 nm and was employed for transmitance/absorbance measurements of liquids with a transflectance optical bundle probe with total optical path of 5 mm and a circular area of 0.5 $\textrm{cm}^2$. Model 2030 scans the rage 1100 2400 nm and was employed for reflectance measurement of solids drugs. 300 spectra, acquired in about 20 s, were averaged for each sample. Chemometric treatment of the spectral data, modelling and classification were performed by using the Unscrambler 7.5 software (CAMO Norway). This package provides the Principal Component Analysis (PCA) and SIMCA algorithms, used for modelling and classification, respectively. Initially, NIRS was evaluated for spectrum acquisition of various drugs, selected in order to accomplish the diversity of physico-chemical characteristics found among commercial products. Parameters which could affect the spectra of a given drug (especially if presented as solid tablets) were investigated and the results showed that the first derivative can minimize spectral changes associated with tablet geometry, physical differences in their faces and position in relation to the probe beam. The effect of ambient humidity and temperature were also investigated. The first factor needs to be controlled for model construction because the ambient humidity can cause spectral alterations that should cause the wrong classification of a real drug if the factor is not considered by the model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.6
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• pp.2186-2196
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• 2014

Spam mail is one of the most general mail dysfunctions, which may cause psychological damage to internet users. As internet usage increases, the amount of spam mail has also gradually increased. Indiscriminate sending, in particular, occurs when spam mail is sent using smart phones or tablets connected to wireless networks. Spam mail consists of approximately 68% of mail traffic; however, it is believed that the true percentage of spam mail is at a much more severe level. In order to analyze and detect spam mail, we introduce a technique based on spam mail characteristics and text mining; in particular, spam mail is detected by extracting the linguistic analysis and language processing. Existing spam mail is analyzed, and hidden spam signatures are extracted using text clustering. Our proposed method utilizes a text mining system to improve the detection and error detection rates for existing spam mail and to respond to new spam mail types.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.229-236
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• 2017

This paper presents an efficient object-based color image-retrieval algorithm that is suitable for the classification and retrieval of images from small to mid-scale datasets, such as images in PCs, tablets, phones, and cameras. The proposed method first finds salient regions by using regional feature vectors, and also finds several dominant colors in each region. Then, each salient region is partitioned into small sub-blocks, which are assigned 1 or 0 with respect to the number of pixels corresponding to a dominant color in the sub-block. This gives a binary map for the dominant color, and this process is repeated for the predefined number of dominant colors. Finally, we have several binary maps, each of which corresponds to a dominant color in a salient region. Hence, the binary maps represent the spatial distribution of the dominant colors in the salient region, and the union (OR operation) of the maps can describe the approximate shapes of salient objects. Also proposed in this paper is a matching method that uses these binary maps and which needs very few computations, because most operations are binary. Experiments on widely used color image databases show that the proposed method performs better than state-of-the-art and previous color-based methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.584-590
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• 2020

Long-term use of digital screens in daily life can lead to computer vision syndrome including symptoms such as eye strain, dry eyes, and headaches. To prevent computer vision syndrome, it is important to limit screen usage time and take frequent breaks. There are a variety of applications that can help users know the screen usage time. However, these apps are limited because users see various screens such as desktops, laptops, and tablets as well as smartphone screens. In this paper, we propose and evaluate machine learning-based models that detect the screen device in use using color, IMU and lidar sensor data. Our evaluation shows that neural network-based models show relatively high F1 scores compared to traditional machine learning models. Among neural network-based models, the MLP and CNN-based models have higher scores than the LSTM-based model. The RF model shows the best result among the traditional machine learning models, followed by the SVM model.