• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.39-52
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• 2005

In this paper, a registration method is presented to register partial 3D point clouds, acquired from a multi-view camera, for 3D reconstruction of an indoor environment. In general, conventional registration methods require a high computational complexity and much time for registration. Moreover, these methods are not robust for 3D point cloud which has comparatively low precision. To overcome these drawbacks, a projection-based registration method is proposed. First, depth images are refined based on temporal property by excluding 3D points with a large variation, and spatial property by filling up holes referring neighboring 3D points. Second, 3D point clouds acquired from two views are projected onto the same image plane, and two-step integer mapping is applied to enable modified KLT (Kanade-Lucas-Tomasi) to find correspondences. Then, fine registration is carried out through minimizing distance errors based on adaptive search range. Finally, we calculate a final color referring colors of corresponding points and reconstruct an indoor environment by applying the above procedure to consecutive scenes. The proposed method not only reduces computational complexity by searching for correspondences on a 2D image plane, but also enables effective registration even for 3D points which have low precision. Furthermore, only a few color and depth images are needed to reconstruct an indoor environment.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.681-688
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• 2015

The k nearest neighbor (k-NN) graph construction is an important operation with many web-related applications, including collaborative filtering, similarity search, and many others in data mining and machine learning. Despite its many elegant properties, the brute force k-NN graph construction method has a computational complexity of $O(n^2)$, which is prohibitive for large scale data sets. Thus, (Key, Value)-based distributed framework, MapReduce, is gaining increasingly widespread use in Locality Sensitive Hashing which is efficient for high-dimension and sparse data. Based on the two-stage strategy, we engage the locality sensitive hashing technique to divide users into small subsets, and then calculate similarity between pairs in the small subsets using a brute force method on MapReduce. Specifically, generating a candidate group stage is important since brute-force calculation is performed in the following step. However, existing methods do not prevent large candidate groups. In this paper, we proposed an efficient algorithm for approximate k-NN graph construction by regrouping candidate groups. Experimental results show that our approach is more effective than existing methods in terms of graph accuracy and scan rate.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.111-126
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• 2017

Recommender systems based on association rule mining significantly contribute to seller's sales by reducing consumers' time to search for products that they want. Recommendations based on the frequency of transactions such as orders can effectively screen out the products that are statistically marketable among multiple products. A product with a high possibility of sales, however, can be omitted from the recommendation if it records insufficient number of transactions at the beginning of the sale. Products missing from the associated recommendations may lose the chance of exposure to consumers, which leads to a decline in the number of transactions. In turn, diminished transactions may create a vicious circle of lost opportunity to be recommended. Thus, initial sales are likely to remain stagnant for a certain period of time. Products that are susceptible to fashion or seasonality, such as clothing, may be greatly affected. This study was aimed at expanding association rules to include into the list of recommendations those products whose initial trading frequency of transactions is low despite the possibility of high sales. The particular purpose is to predict the strength of the direct connection of two unconnected items through the properties of the paths located between them. An association between two items revealed in transactions can be interpreted as the interaction between them, which can be expressed as a link in a social network whose nodes are items. The first step calculates the centralities of the nodes in the middle of the paths that indirectly connect the two nodes without direct connection. The next step identifies the number of the paths and the shortest among them. These extracts are used as independent variables in the regression analysis to predict future connection strength between the nodes. The strength of the connection between the two nodes of the model, which is defined by the number of nodes between the two nodes, is measured after a certain period of time. The regression analysis results confirm that the number of paths between the two products, the distance of the shortest path, and the number of neighboring items connected to the products are significantly related to their potential strength. This study used actual order transaction data collected for three months from February to April in 2016 from an online commerce company. To reduce the complexity of analytics as the scale of the network grows, the analysis was performed only on miscellaneous goods. Two consecutively purchased items were chosen from each customer's transactions to obtain a pair of antecedent and consequent, which secures a link needed for constituting a social network. The direction of the link was determined in the order in which the goods were purchased. Except for the last ten days of the data collection period, the social network of associated items was built for the extraction of independent variables. The model predicts the number of links to be connected in the next ten days from the explanatory variables. Of the 5,711 previously unconnected links, 611 were newly connected for the last ten days. Through experiments, the proposed model demonstrated excellent predictions. Of the 571 links that the proposed model predicts, 269 were confirmed to have been connected. This is 4.4 times more than the average of 61, which can be found without any prediction model. This study is expected to be useful regarding industries whose new products launch quickly with short life cycles, since their exposure time is critical. Also, it can be used to detect diseases that are rarely found in the early stages of medical treatment because of the low incidence of outbreaks. Since the complexity of the social networking analysis is sensitive to the number of nodes and links that make up the network, this study was conducted in a particular category of miscellaneous goods. Future research should consider that this condition may limit the opportunity to detect unexpected associations between products belonging to different categories of classification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5350-5355
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• 2012

Human tyrosinase (hTyr) catalyzes the first and rate limiting step in the biosynthesis of a skin color determinant, melanin. Although a number of cosmetic companies have tried to develop hTyr inhibitors for several decades, absence of 3D structure of hTyr make it impossible to design or screen inhibitors by structure-based approach. Therefore, we built a 3D structure by comparative modeling technique based on the crystal structure of tyrosinase from Bacillus megaterium to provide structural information and to search new hit compounds from database. Our model revealed that two copper atoms of active site located deep inside and were coordinated with six strictly conserved histidine residues coming from four-helix-bundle. Substrate binding site had narrow funnel like shape and its entrance was wide and exposed to solvent. In addition, hTyr-tyrosine and hTyr-kojic acid, a well-known inhibitor, complexes were modeled with the guide of solvent accessible surface generated by in-house software. Our model demonstrated that only phenol group or its analogs could fill the binding site near the nuclear copper center, because inside of binding site had narrow shape relatively. In conclusion, the results of this study may provide helpful information for designing and screening new anti-melanogenic agents.

• KIPS Transactions on Computer and Communication Systems
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• v.6 no.9
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• pp.377-384
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• 2017

The performance improvement of a single core processor has reached its limit since the circuit density cannot be increased any longer due to overheating. Therefore, the multicore and manycore architectures have emerged as viable approaches and parallel programming becomes more important. Haskell, a purely functional language, is getting popular in this situation since it naturally supports parallel programming owing to its beneficial features including the implicit parallelism in evaluating expressions and the monadic tools supporting parallel constructs. However, the performance of Haskell parallel programs is strongly influenced by the performance of the run-time system including the garbage collector. Though a memory profiling tool namely GC-tune has been suggested, we need a more systematic way to use this tool. Since GC-tune finds the optimal memory size by executing the target program with all the different possible GC options, the GC-tuning time takes too long. This paper suggests a basic divide-and-conquer method to reduce the number of GC-tune executions by reducing the search area by one-quarter for every searching step. Applying this method to two parallel programs, a maximally independent set and a K-means programs, the memory tuning time is reduced by 7.78 times with accuracy 98% on average.

• Journal of Korean Medicine Rehabilitation
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• v.32 no.3
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• pp.13-27
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• 2022

Objectives This study investigated the trends in domestic and international clinical research in craniosacral therapy, classified as a type of Chuna manual therapy, and suggested further directions in Korean medicine. Methods This scoping review was performed using the Arksey and O'Malley methodological framework and preferred reporting items as per the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews checklist. Eight electronic databases (PubMed, EMBASE, Cochrane Library, Koreanstudies Information Service System [KISS], KMBASE, Oriental Medicine Advanced Searching Integrated System [OASIS], Research Information Sharing Service [RISS], ScienceON) were searched to identify articles with the search terms "craniosacral therapy" and "cranial osteopathy" until December 2021. Results Forty-five studies were eligible as per our inclusion criteria. Most research studies (n=44) were conducted in the field of medicine and pharmacy, especially in rehabilitation medicine (n=16). As a result of the study design, randomized controlled trials (n=20) were the most common, and chronic pain (n=9) was the most frequently targeted disease, followed by headache (n=7). Thirty-two studies suggested interventions and 20 studies used Upledger's 10-step protocol. The average duration of craniosacral therapy was 41 min per session, administered 1.4 times per week. Outcome measurements were analyzed and categorized with the examination procedure for the patient. Conclusions This is the first scoping review of craniosacral therapy in Korea, and we believe that our findings could support its utility as Chuna. In the future, more studies should be conducted to establish the evidence of clinical efficacy of craniosacral therapy and develop standard techniques in Korean medicine.

• Journal of Intelligence and Information Systems
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• v.20 no.3
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• pp.59-76
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• 2014

The revolution of smart media such as smart phone, smart TV and tablets has brought easiness for people to get contents and related information anywhere and anytime. The characteristics of the smart media have changed user behavior for watching the contents from passive attitude into active one. Video is a kind of multimedia resources and widely used to provide information effectively. People not only watch video contents, but also search for related information to specific objects appeared in the contents. However, people have to use extra views or devices to find the information because the existing video contents provide no information through the contents. Therefore, the interaction between user and media is becoming a major concern. The demand for direct interaction and instant information is much increasing. Digital media environment is no longer expected to serve as a one-way information service, which requires user to search manually on the internet finding information they need. To solve the current inconvenience, an interactive service is needed to provide the information exchange function between people and video contents, or between people themselves. Recently, many researchers have recognized the importance of the requirements for interactive services, but only few services provide interactive video within restricted functionality. Only cooking domain is chosen for an interactive cooking video query service in this research. Cooking is receiving lots of people attention continuously. By using smart media devices, user can easily watch a cooking video. One-way information nature of cooking video does not allow to interactively getting more information about the certain contents, although due to the characteristics of videos, cooking videos provide various information such as cooking scenes and explanation for each recipe step. Cooking video indeed attracts academic researches to study and solve several problems related to cooking. However, just few studies focused on interactive services in cooking video and they still not sufficient to provide the interaction with users. In this paper, an interactive cooking video query service system with linked data to provide the interaction functionalities to users. A linked recipe schema is used to handle the linked data. The linked data approach is applied to construct queries in systematic manner when user interacts with cooking videos. We add some classes, data properties, and relations to the linked recipe schema because the current version of the schema is not enough to serve user interaction. A web crawler extracts recipe information from allrecipes.com. All extracted recipe information is transformed into ontology instances by using developed instance generator. To provide a query function, hundreds of questions in cooking video web sites such as BBC food, Foodista, Fine cooking are investigated and analyzed. After the analysis of the investigated questions, we summary the questions into four categories by question generalization. For the question generalization, the questions are clustered in eleven questions. The proposed system provides an environment associating UI (User Interface) and UX (User Experience) that allow user to watch cooking videos while obtaining the necessary additional information using extra information layer. User can use the proposed interactive cooking video system at both PC and mobile environments because responsive web design is applied for the proposed system. In addition, the proposed system enables the interaction between user and video in various smart media devices by employing linked data to provide information matching with the current context. Two methods are used to evaluate the proposed system. First, through a questionnaire-based method, computer system usability is measured by comparing the proposed system with the existing web site. Second, the answer accuracy for user interaction is measured to inspect to-be-offered information. The experimental results show that the proposed system receives a favorable evaluation and provides accurate answers for user interaction.

• The Korean Journal of Nuclear Medicine Technology
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• v.12 no.1
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• pp.19-26
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• 2008

Purpose: In general, We discharged radioactive wastewater and sewages less than $8.1{\times}10^{-13}$ Ci/ml in a exclusive water-purifier tank. Our hospital operating three exclusive water-purifier tank for radioactive wastewater and sewages of 60 tons capacity respectively. In order to meet the criteria it need a enough decay more than 125 days per each exclusive tank. However, recently we fell into the serious situation that decay period was decreased remarkably, owing to the wastewater amount increased rapidly by enlarge the therapy ward. For that reason, in this article, I'd like to say the way that reducing of radioactive wastewater and sewages rationally. Materials and Methods: From January, 2006 to October, four hundred and two cases were analyzed. They were all hospitalized during 3 days and 2 nights. We calculated the average amount of water used (include toilet water used, shower water used, washstand water used, $\cdots$), each exclusive water-purifier tank's decay period, as well as try to search the increased factors about water-purifier tank inflow flux by re-analysis of the procedure of radioisotope therapy step by step. Results: We could increase each exclusive water-purifier tank's decay period from 84 days to 130 days through the improvement about following cause: (1) Improvement of conventional toilet stool for excessive water waste $\rightarrow$ Replacement of water saving style toilet stool (2) Prevention of unnecessary shower and wash (3) Stop the diuretics taking during hospitalization (4) Analysis of relationship between water intakes and residual dose of body (5) Education about outside toilet utilization before the administration (6) Changed each water-purifier tank's maximum level from85% to 90% Conclusion: The originality of our efforts are not only software but hardware performance improvements. Incidentally the side of software's are change of therapy procedures and protocols, the side of hardware's are replacement of water saving style toilet stool and change of each water-purifier tank's maximum level. Thus even if a long lapse of time, problem such as return to the former conditions may not happen. Besides, We expect that our trials become a new reasonable model in similar situation.

• 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 the korean academy of Pediatric Dentistry
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• v.32 no.1
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• pp.75-88
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• 2005

Ameloblastoma is the most commonly occurring odontogenic tumor in oral cavity. Although most are benign epithelial neoplasm, they are generally considered to be locally aggressive and destructive, exhibiting a high rate of recurrence. The biological behavior of this neoplasm is a slowly growing, locally invasive tumor without metastasis, therefore malignant neoplasm, changed its histological appearance to carcinoma or showed distant metastasis, is only defined clinically. In this study, we identified the differentially expressed genes(DEGs) in stages under benign or malignant ameloblastoma compared with normal patient using ordered differential display(ODD) reverse transcription(RT)-PCR and $GeneFishing^{TM}$ technology. ODD RT-PCR is rather effective when the investigation of samples containing very small amounts of total RNA must be accomplished. ODD RT-PCR used the means of amplification with anchored T-primer and adaptor specific primer. bearing definite two bases at their 3' ends and so this method could display differential 3'-expressed sequence taqs(ESTs) patterns without using full-length cDNAs. Compared with standard differential display, ODD RT-PCR is more simple and have enough sensitivity to search for molecular markers by comparing gene expression profiles, However, this method required much effort and skill to perform. $GeneFishing^{TM}$ modified from DD-PCR is an improved method for detecting differentially expressed genes in two or more related samples. This two step RT-PCR method uses a constant reverse primer(anchor ACP-T) to prime the RT reaction and arbitrary primer pairs(annealing control primers, ACPs) during PCR. Because of high annealing specificity of ACPs than ODD RT-PCR, the application of $GeneFishing^{TM}$ to DEG discovery generates reproducible, authentic, and long(100bp to 2kb) PCR products that are detectable on agarose gels. Consequently, various DEGs observed differential expression levels on agarose gels were isolated from normal, benign, and malignant tissues using these methods. The expression patterns of the some isolated DEGs through ODD RT-PCR and $GeneFishing^{TM}$ were confirmed by Northern blot analysis and RT-PCR. The results showed that these identified DEGs were implicated in ameloblastoma neoplasm processes. Therefore, the identified DEGs will be further studied in order to be applied in candidate selection for marker as an early diagnosis during ameloblastoma neoplasm processes.