• The Korean Journal of Nuclear Medicine
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• v.30 no.4
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• pp.548-559
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• 1996

A series of performance measurements of positron emission tomography (PET) were performed following the recommendations of the Computer and Instrumentation Council of the Society of Nuclear Medicine and the National Electrical Manufacturers Association. We investigated the performance of the General Electric $Advance^{TM}$ PET. The measurements include the basic intrinsic tests of spatial resolution, scatter fraction, sensitivity, and count rate losses and randoms. They also include the tests of the accuracy of corrections: count rate linearity correction, uniformity correction, scatter correction and attenuation correction. GE $Advance^{TM}$ PET has bismuth germanate oxide crystals (4.0mm transaxial ${\times}$ 8.1mm axial ${\times}$ 30.0mm radial) in 18 rings, which form 35 imaging planes spaced by 4.25mm. The system has retractable tungsten septa 1mm thick and 12cm long. Transaxial resolution was 4.92mm FWHM in 2D and 5.14mm FWHM in 3D at the center. Average axial resolution in 2D decreased from 3.91mm FWHM at the center to 6.49mm FWHM at R=20cm. Average scatter fraction of direct and cross slices was 9.57%. Dead-time losses of 50% corresponded to a radioactivity concentration of $4.86{\mu}Ci/cc$ and a true count rate of 519 kcps in 2D. The accuracy of count rate linearity correction was 1.84% at the activity of $4.50{\mu}Ci/cc$. Non-uniformity was 2.06% in 2D and 2.93% in 3D. Remnant errors after scatter correction were 0.55% in 2D and 4.12% in 3D. The errors of attenuation correction were 6.21% (air), 0.20% (water), -6.32% (teflon) in 2D and 5.00% (air), 6.94% (water), 3.01% (teflon) in 3D. The results indicate the performance of GE $Advance^{TM}$ PET scanner to be well suited for clinical and research applications.

• Journal of Wetlands Research
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• v.12 no.1
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• pp.95-103
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• 2010

Discharge data examine the process of hydrologic cycle and used significantly in water resource planning and irrigation and flood control planning. However, it needs lots of time and money to get the discharge data. So discharge rating curve is usually used in converting discharge data. Therefore reliability of discharge rating curve absolutely depends on quality of discharge data. Many engineers who study hydrologic engineering make high quality discharge data to develop reliable discharge rating curve. And they carry out research on standard and method of discharge measurement, and equipment improvement. Now various flow meters are utilized to make discharge data in Korea. However, accuracy of equipment and experimental research data from measurement are not enough. In this paper, constant discharge flowed through standard concrete channel, and the velocity is measured using various flow meters. Also Discharge is calculated by measured data to compare and analyze. The equipment for the experiment is Price AA(USGS Type AA Current meter), flow meter, ADC, C2 small current meter, flow tracker, Electromagnetic current meter. The discharge got form various flow meters which are widely used for discharge measurement. The various depths of water were examined and compared such as 0.30 m, 0.35 m, 0.40 m, 0.45 m, 0.50 m, 0.55 m. The experiment progresses a round-measurement on 6-case. Wading measurement(one point method : the 60 % height in surface of the water) was applied to improve creditability and accuracy among measurement methods. USGS Type AA current Meter, Flow Meter, ADC, C2 Small Current meter got the certificate of quality guaranteed. So the results of experiment were used to compare discharge. The Results showed the difference based on USGS Type AA current Meter at average discharge and velocity. Electromagnetic current meter made differences over $\pm$ 10 % and Flow Meter made differences under $\pm$ 10 %. Also ADC, Flow Meter, C2 Small Current meter made differences under $\pm$ 5 %.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.175-197
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• 2021

Recently, with the development of deep learning technology, research on unstructured data analysis is being actively conducted, and it is showing remarkable results in various fields such as classification, summary, and generation. Among various text analysis fields, text classification is the most widely used technology in academia and industry. Text classification includes binary class classification with one label among two classes, multi-class classification with one label among several classes, and multi-label classification with multiple labels among several classes. In particular, multi-label classification requires a different training method from binary class classification and multi-class classification because of the characteristic of having multiple labels. In addition, since the number of labels to be predicted increases as the number of labels and classes increases, there is a limitation in that performance improvement is difficult due to an increase in prediction difficulty. To overcome these limitations, (i) compressing the initially given high-dimensional label space into a low-dimensional latent label space, (ii) after performing training to predict the compressed label, (iii) restoring the predicted label to the high-dimensional original label space, research on label embedding is being actively conducted. Typical label embedding techniques include Principal Label Space Transformation (PLST), Multi-Label Classification via Boolean Matrix Decomposition (MLC-BMaD), and Bayesian Multi-Label Compressed Sensing (BML-CS). However, since these techniques consider only the linear relationship between labels or compress the labels by random transformation, it is difficult to understand the non-linear relationship between labels, so there is a limitation in that it is not possible to create a latent label space sufficiently containing the information of the original label. Recently, there have been increasing attempts to improve performance by applying deep learning technology to label embedding. Label embedding using an autoencoder, a deep learning model that is effective for data compression and restoration, is representative. However, the traditional autoencoder-based label embedding has a limitation in that a large amount of information loss occurs when compressing a high-dimensional label space having a myriad of classes into a low-dimensional latent label space. This can be found in the gradient loss problem that occurs in the backpropagation process of learning. To solve this problem, skip connection was devised, and by adding the input of the layer to the output to prevent gradient loss during backpropagation, efficient learning is possible even when the layer is deep. Skip connection is mainly used for image feature extraction in convolutional neural networks, but studies using skip connection in autoencoder or label embedding process are still lacking. Therefore, in this study, we propose an autoencoder-based label embedding methodology in which skip connections are added to each of the encoder and decoder to form a low-dimensional latent label space that reflects the information of the high-dimensional label space well. In addition, the proposed methodology was applied to actual paper keywords to derive the high-dimensional keyword label space and the low-dimensional latent label space. Using this, we conducted an experiment to predict the compressed keyword vector existing in the latent label space from the paper abstract and to evaluate the multi-label classification by restoring the predicted keyword vector back to the original label space. As a result, the accuracy, precision, recall, and F1 score used as performance indicators showed far superior performance in multi-label classification based on the proposed methodology compared to traditional multi-label classification methods. This can be seen that the low-dimensional latent label space derived through the proposed methodology well reflected the information of the high-dimensional label space, which ultimately led to the improvement of the performance of the multi-label classification itself. In addition, the utility of the proposed methodology was identified by comparing the performance of the proposed methodology according to the domain characteristics and the number of dimensions of the latent label space.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.27-42
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• 2020

Traditional companies with offline stores were unable to secure large display space due to the problems of cost. This limitation inevitably allowed limited kinds of products to be displayed on the shelves, which resulted in consumers being deprived of the opportunity to experience various items. Taking advantage of the virtual space called the Internet, online shopping goes beyond the limits of limitations in physical space of offline shopping and is now able to display numerous products on web pages that can satisfy consumers with a variety of needs. Paradoxically, however, this can also cause consumers to experience the difficulty of comparing and evaluating too many alternatives in their purchase decision-making process. As an effort to address this side effect, various kinds of consumer's purchase decision support systems have been studied, such as keyword-based item search service and recommender systems. These systems can reduce search time for items, prevent consumer from leaving while browsing, and contribute to the seller's increased sales. Among those systems, recommender systems based on association rule mining techniques can effectively detect interrelated products from transaction data such as orders. The association between products obtained by statistical analysis provides clues to predicting how interested consumers will be in another product. However, since its algorithm is based on the number of transactions, products not sold enough so far in the early days of launch may not be included in the list of recommendations even though they are highly likely to be sold. Such missing items may not have sufficient opportunities to be exposed to consumers to record sufficient sales, and then fall into a vicious cycle of a vicious cycle of declining sales and omission in the recommendation list. This situation is an inevitable outcome in situations in which recommendations are made based on past transaction histories, rather than on determining potential future sales possibilities. This study started with the idea that reflecting the means by which this potential possibility can be identified indirectly would help to select highly recommended products. In the light of the fact that the attributes of a product affect the consumer's purchasing decisions, this study was conducted to reflect them in the recommender systems. In other words, consumers who visit a product page have shown interest in the attributes of the product and would be also interested in other products with the same attributes. On such assumption, based on these attributes, the recommender system can select recommended products that can show a higher acceptance rate. Given that a category is one of the main attributes of a product, it can be a good indicator of not only direct associations between two items but also potential associations that have yet to be revealed. Based on this idea, the study devised a recommender system that reflects not only associations between products but also categories. Through regression analysis, two kinds of associations were combined to form a model that could predict the hit rate of recommendation. To evaluate the performance of the proposed model, another regression model was also developed based only on associations between products. Comparative experiments were designed to be similar to the environment in which products are actually recommended in online shopping malls. First, the association rules for all possible combinations of antecedent and consequent items were generated from the order data. Then, hit rates for each of the associated rules were predicted from the support and confidence that are calculated by each of the models. The comparative experiments using order data collected from an online shopping mall show that the recommendation accuracy can be improved by further reflecting not only the association between products but also categories in the recommendation of related products. The proposed model showed a 2 to 3 percent improvement in hit rates compared to the existing model. From a practical point of view, it is expected to have a positive effect on improving consumers' purchasing satisfaction and increasing sellers' sales.

• Korean Journal of Clinical Laboratory Science
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• v.37 no.2
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• pp.65-70
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• 2005

Those specifications of the CLIA analytical tolerance limits are consistent with the performance goals in Six Sigma Quality Management. Six sigma analysis determines performance quality from bias and precision statistics. It also shows if the method meets the criteria for the six sigma performance. Performance standards calculates allowable total error from several different criteria. Six sigma means six standard deviations from the target value or mean value and about 3.4 failures per million opportunities for failure. Sigma Quality Level is an indicator of process centering and process variation total error allowable. Tolerance specification is replaced by a Total Error specification, which is a common form of a quality specification for a laboratory test. The CLIA criteria for acceptable performance in proficiency testing events are given in the form of an allowable total error, TEa. Thus there is a published list of TEa specifications for regulated analytes. In terms of TEa, Six Sigma Quality Management sets a precision goal of TEa/6 and an accuracy goal of 1.5 (TEa/6). This concept is based on the proficiency testing specification of target value +/-3s, TEa from reference intervals, biological variation, and peer group median mean surveys. We have found rules to calculate as a fraction of a reference interval and peer group median mean surveys. We studied to develop total error allowable from peer group survey results and CLIA 88 rules in US on 19 items TP, ALB, T.B, ALP, AST, ALT, CL, LD, K, Na, CRE, BUN, T.C, GLU, GGT, CA, phosphorus, UA, TG tests in chematology were follows. Sigma level versus TEa from peer group median mean CV of each item by group mean were assessed by process performance, fitting within six sigma tolerance limits were TP ($6.1{\delta}$/9.3%), ALB ($6.9{\delta}$/11.3%), T.B ($3.4{\delta}$/25.6%), ALP ($6.8{\delta}$/31.5%), AST ($4.5{\delta}$/16.8%), ALT ($1.6{\delta}$/19.3%), CL ($4.6{\delta}$/8.4%), LD ($11.5{\delta}$/20.07%), K ($2.5{\delta}$/0.39mmol/L), Na ($3.6{\delta}$/6.87mmol/L), CRE ($9.9{\delta}$/21.8%), BUN ($4.3{\delta}$/13.3%), UA ($5.9{\delta}$/11.5%), T.C ($2.2{\delta}$/10.7%), GLU ($4.8{\delta}$/10.2%), GGT ($7.5{\delta}$/27.3%), CA ($5.5{\delta}$/0.87mmol/L), IP ($8.5{\delta}$/13.17%), TG ($9.6{\delta}$/17.7%). Peer group survey median CV in Korean External Assessment greater than CLIA criteria were CL (8.45%/5%), BUN (13.3%/9%), CRE (21.8%/15%), T.B (25.6%/20%), and Na (6.87mmol/L/4mmol/L). Peer group survey median CV less than it were as TP (9.3%/10%), AST (16.8%/20%), ALT (19.3%/20%), K (0.39mmol/L/0.5mmol/L), UA (11.5%/17%), Ca (0.87mg/dL1mg/L), TG (17.7%/25%). TEa in 17 items were same one in 14 items with 82.35%. We found out the truth on increasing sigma level due to increased total error allowable, and were sure that the goal of setting total error allowable would affect the evaluation of sigma metrics in the process, if sustaining the same process.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.36 no.3
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• pp.128-136
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• 2018

This study was centered on the protested data of the issue of "History of Landscape Architecture" in the handwritten course of landscaping articles of National Qualifications Test. The purpose of this study is to examine the types of social problems in the process of correcting erroneous historical facts. The purpose of this study was to find alternatives for the development of the field of landscape and culture history that can assist in the verification of the historical facts of the landscape sciences examination questions. The main results are as follows. First, as a result of analyzing the contents of the landscape architects' subject matter, the establishment of concept of landscape style and form and the confirmation of historical facts were investigated as important types to be established for development of landscape landscape history department. It seems that the social consensus of the expert group is needed to supplement the lack of data to refer to landscape architectural theory. Second, the analysis of the problematic narrative contents resulted in a total of five types of questionnaires. The appeared in the Undefined style and form(52.94%), Unproven historical facts(25.13%), Obscurity Era classification(11.77%), Lack of specificity(6.95%), Content scope of obscurity events(3.21%) Third, it is not only the lack of information to learn the theory by comparing and analyzing the contents of the statements in the landscape architect 's question items, but also the difference of contents between books was analyzed as the main cause of the problem. As a result of examining the characteristics and examples of the issues raised in landscape architectural problems, it was related to the social phenomenon, and it was classified into cultural factors and political factors. Fourth, the resolution of problematic issues in landscape architects' landscaping articles, which are national technical qualification tests, shows positive results. The information determined in the process of solving the perceived content can be used directly in landscaping field, and it helps the accuracy of the verification process by identifying the types and characteristics of the issues.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.6
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• pp.19-25
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• 2019

The CleanSYS(Clean SYStem) is operated to monitor air pollutants emitted from specific industrial complexes in Korea. So the industrial complexes without the system are directly monitored by the control officers. For efficient monitoring, studies using various sensors have been conducted to monitor air pollutants emitted from industrial complex. In this study, hyperspectral sensors were used to model and verify the equations for estimating the concentration of $NO_2$(nitrogen dioxide) in air pollutants emitted. For development of the equations, spectral radiance were observed for $NO_2$ at various concentrations with different SZA(Solar Zenith Angle), VZA(Viewing Zenith Angle), and RAA(Relative Azimuth Angle). From the observed spectral radiance, the calculated value of the difference between the values of the specific wavelengths was taken as an absorption depth, and the equations were developed using the relationship between the depth and the $NO_2$ concentration. The spectral radiance mixed gas of $NO_2$ and $SO_2$(sulfur dioxide) was used to verify the equations. As a result, the $R^2$(coefficient of determination) and RMSE(Root Mean Square Error) were different from 0.71~0.88 and 72~23 ppm according to the form of the equation, and $R^2$ of the exponential form was the highest among the equations. Depending on the type of the equations, the accuracy of the estimated concentration with varying concentrations is not constant. However, if the equations are advanced in the future, hyperspectral sensors can be used to monitor the $NO_2$ emitted from the industrial complex.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.57-78
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• 2020

With the development of the Internet, consumers have had an opportunity to check product information easily through E-Commerce. Product reviews used in the process of purchasing goods are based on user experience, allowing consumers to engage as producers of information as well as refer to information. This can be a way to increase the efficiency of purchasing decisions from the perspective of consumers, and from the seller's point of view, it can help develop products and strengthen their competitiveness. However, it takes a lot of time and effort to understand the overall assessment and assessment dimensions of the products that I think are important in reading the vast amount of product reviews offered by E-Commerce for the products consumers want to compare. This is because product reviews are unstructured information and it is difficult to read sentiment of reviews and assessment dimension immediately. For example, consumers who want to purchase a laptop would like to check the assessment of comparative products at each dimension, such as performance, weight, delivery, speed, and design. Therefore, in this paper, we would like to propose a method to automatically generate multi-dimensional product assessment scores in product reviews that we would like to compare. The methods presented in this study consist largely of two phases. One is the pre-preparation phase and the second is the individual product scoring phase. In the pre-preparation phase, a dimensioned classification model and a sentiment analysis model are created based on a review of the large category product group review. By combining word embedding and association analysis, the dimensioned classification model complements the limitation that word embedding methods for finding relevance between dimensions and words in existing studies see only the distance of words in sentences. Sentiment analysis models generate CNN models by organizing learning data tagged with positives and negatives on a phrase unit for accurate polarity detection. Through this, the individual product scoring phase applies the models pre-prepared for the phrase unit review. Multi-dimensional assessment scores can be obtained by aggregating them by assessment dimension according to the proportion of reviews organized like this, which are grouped among those that are judged to describe a specific dimension for each phrase. In the experiment of this paper, approximately 260,000 reviews of the large category product group are collected to form a dimensioned classification model and a sentiment analysis model. In addition, reviews of the laptops of S and L companies selling at E-Commerce are collected and used as experimental data, respectively. The dimensioned classification model classified individual product reviews broken down into phrases into six assessment dimensions and combined the existing word embedding method with an association analysis indicating frequency between words and dimensions. As a result of combining word embedding and association analysis, the accuracy of the model increased by 13.7%. The sentiment analysis models could be seen to closely analyze the assessment when they were taught in a phrase unit rather than in sentences. As a result, it was confirmed that the accuracy was 29.4% higher than the sentence-based model. Through this study, both sellers and consumers can expect efficient decision making in purchasing and product development, given that they can make multi-dimensional comparisons of products. In addition, text reviews, which are unstructured data, were transformed into objective values such as frequency and morpheme, and they were analysed together using word embedding and association analysis to improve the objectivity aspects of more precise multi-dimensional analysis and research. This will be an attractive analysis model in terms of not only enabling more effective service deployment during the evolving E-Commerce market and fierce competition, but also satisfying both customers.

• Journal of Science of Art and Design
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• v.11
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• pp.221-249
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• 2007

Today, photography is facing to the crisis of identity and dilemma of ontology from the digital imaging process in the new technology form. It is very important points to say rethinking of the traditional photographic medium, that has changed the way we view the world and ourselves is perhaps an understatement and that photography has transformed our essential understanding of reality. Now, no longer are photographic images regarded as the true automatic recording, innocent evidence and the mirror to the reality. Rather, photography constructs the world for our entertainment, helping to create the comforting illusions by which we live. The recognition that photographs are not constructions and reflections of reality, is the basis for the actual presence within the contemporary photographic world. It is shock. This thesis's aim is to look for the problems of photographic identity and ontological crisis that is controlling and regulating digital photographic imagery, allowing the reproduction of the electronic simulations era. Photography loses its special aesthetic status and becomes no more true information and, exclusively evidence by traditional film and paper that appeared both as a technological accuracy and as a medium-specific aesthetic. The result, photography is facing two crises, one is the photographic ontology(the introduction of computerized digital images) and the other is photographic epistemology(having to do broader changes in ethics, knowledge and culture). Taken together, these crises apparently threaten us with the death of photography, with the 'end' of photography and the culture it sustains. The thesis's meaning is to look into the dilemma of photography's ontology and epistemology, especially, automatical index and digital codes from its origin, meaning, and identity as the technological medium. Thus, in particular, thesis focuses on the analog imagery presence, from the nature in the material world, and the digital imagery presence from the cultural situations in our society. And also thesis's aim is to examine the main issues of the history of photography has been concentrated on the ontological arguments since the discovery of photography in 1839. Photography has never been only one static technology form. Rather, its nearly two centuries of technological development have been marked by numerous, competing of technological innovation and self revolution from the dual aspects. This thesis examines recent account of photography by the analysis of the medium's concept, meaning, identity between film base image and digital base image from the aspects of photographic ontology and epistemology. Thus, the structure of thesis is fairy straightforward to examine what appear to be two opposing view of photographic conditions and ontological situations. Thesis' view contrasts that figure out the value of photography according to its fundamental characteristic as a medium. Also, it seeks a possible solution to the dilemma of photographic ontology through the medium's origin from the early years of the nineteenth century to the raising questions about the different meaning(analog/digital) of photography, now. Finally, this thesis emphasizes and concludes that the photographic ontological crisis reflects to the paradoxical dynamic structure, that unsolved the origins of the medium, itself. Moreover, even photography is not single identity of the photographic ontology, and also can not be understood as having a static identity or singular status from the dynamic field of technologies, practices, and images.