• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.4
• /
• pp.333-337
• /
• 2002

A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n$\times$n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

• Journal of Biomedical Engineering Research
• /
• v.38 no.2
• /
• pp.82-88
• /
• 2017

Enlargement of the lateral ventricles have been identified as a surrogate marker of neurological disorders. Quantitative measure of the lateral ventricle from MRI would enable earlier and more accurate clinical diagnosis in monitoring disease progression. Even though it requires an automated or semi-automated segmentation method for objective quantification, it is difficult to define lateral ventricles due to insufficient contrast and brightness of structural imaging. In this study, we proposed a fully automated lateral ventricle segmentation method based on a graph cuts algorithm combined with atlas-based segmentation and connected component labeling. Initially, initial seeds for graph cuts were defined by atlas-based segmentation (ATS). They were adjusted by partial volume images in order to provide accurate a priori information on graph cuts. A graph cuts algorithm is to finds a global minimum of energy with minimum cut/maximum flow algorithm function on graph. In addition, connected component labeling used to remove false ventricle regions. The proposed method was validated with the well-known tools using the dice similarity index, recall and precision values. The proposed method was significantly higher dice similarity index ($0.860{\pm}0.036$, p < 0.001) and recall ($0.833{\pm}0.037$, p < 0.001) compared with other tools. Therefore, the proposed method yielded a robust and reliable segmentation result.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.13 no.5
• /
• pp.1043-1050
• /
• 2018

In this paper, we propose a reinforce learning based on cooperative sensing scheme to select optimal secondary users(SUs) to enhance the detection performance of spectrum sensing in Cognitive radio(CR) networks. The SU with high accuracy is identified based on the similarity between the global sensing result obtained through cooperative sensing and the local sensing result of the SU. A fusion center(FC) uses similarity of SUs as reward value for Q-learning to determine SUs which participate in cooperative sensing with accurate sensing results. The experimental results show that the proposed method improves the detection performance compared to conventional cooperative sensing schemes.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.345-352
• /
• 2022

Conducting AI-based fusion business due to the increment of ICT fusion medical device has been expanded. In addition, AI-based medical devices help change existing medical system on treatment into the paradigm of customized treatment such as preliminary diagnosis and prevention. It will be generally promoted to the change of medical device industry. Although the current demand forecasting of medical biotechnology commercialization is based on the method of Delphi and AHP, there is a problem that it is difficult to have a generalization due to fluctuation results according to a pool of participants. Therefore, the purpose of the paper is to predict demand forecasting for identifying promising technology based on building up big data in medical biotechnology. The development method is to employ candidate technologies of keywords extracted from SCOPUS and to use word2vec for drawing analysis indicator, technological distance similarity, and recommended technological similarity of top-level items in order to achieve a reasonable result. In addition, the method builds up academic big data for 5 years (2016-2020) in order to commercialize technology excavation on demand perspective. Lastly, the paper employs global data studies in order to develop domestic and international demand for technology excavation in the medical biotechnology field.

• Journal of information and communication convergence engineering
• /
• v.22 no.1
• /
• pp.44-55
• /
• 2024

High-utility itemset mining (HUIM) is a dominant technology that enables enterprises to make real-time decisions, including supply chain management, customer segmentation, and business analytics. However, classical support value-driven Apriori solutions are confined and unable to meet real-time enterprise demands, especially for large amounts of input data. This study introduces a groundbreaking model for top-N high utility itemset mining in real-time enterprise applications. Unlike traditional Apriori-based solutions, the proposed convolutional sequential embedding metrics-driven cosine-similarity-based multilayer perception learning model leverages global and contextual features, including semantic attributes, for enhanced top-N recommendations over sequential transactions. The MATLAB-based simulations of the model on diverse datasets, demonstrated an impressive precision (0.5632), mean absolute error (MAE) (0.7610), hit rate (HR)@K (0.5720), and normalized discounted cumulative gain (NDCG)@K (0.4268). The average MAE across different datasets and latent dimensions was 0.608. Additionally, the model achieved remarkable cumulative accuracy and precision of 97.94% and 97.04% in performance, respectively, surpassing existing state-of-the-art models. This affirms the robustness and effectiveness of the proposed model in real-time enterprise scenarios.

• Journal of Korea Society of Industrial Information Systems
• /
• v.19 no.4
• /
• pp.53-63
• /
• 2014

Moving context determination is an important issue to be resolved in a mobile computing environment. This paper presents a method for recognizing and classifying a mobile user's moving context by Euclidean distance similarity. In the proposed method, basic data are gathered using Global Positioning System (GPS) and accelerometer sensors, and by using the data, the system decides which moving situation the user is in. The decided situation is one of the four categories: stop, walking, run, and moved by a car. In order to evaluate the effectiveness and feasibility of the proposed scheme, we have implemented applications using several variations of Euclidean distance similarity on the Android system, and measured the accuracies. Experimental results show that the proposed system achieves more than 90% accuracy.

• Korean Journal of Cognitive Science
• /
• v.13 no.2
• /
• pp.25-36
• /
• 2002

Two experiments were performed to explore the effects of the types of property and the tasks on the pattern of property inference in a source selection task in which the source objects were to be selected. animals that were globally similar to the target animal was selected as possible sources when the target properties were anatomical. However animals that were strongly associated with the target property were selected when the target properties were about ability In a passive inference task where premises were given. the global similarity between the source objects and the target object differently affected the confidence of the conclusion depending on the types of the target property: The similarity between the source and the target affected the degree of confidence when the target properties were anatomical ones, but did not affect when the target properties were about ability. The results suggested that participants seemed to have primitive understanding of the relevance of sources to the target properties, but did not. spontaneously seek or use the relevant information.

• Korean Journal of Construction Engineering and Management
• /
• v.23 no.2
• /
• pp.16-23
• /
• 2022

This study examined the trend of international competitiveness over the past 10 years (2011-2020), focusing on comparative analysis with China and the United States, targeting seven major export items of Korean construction machinery based on 6 units of HS code. To this end, the export similarity index and trade competitiveness index were calculated and analyzed using UN Comtrade and Korea International Trade Association trade statistics. As a result of the analysis, competition between Korea and China has intensified over the past decade, and competition with the United States has remained at a certain level. Korean forklifts (8427.20) are exporting to the world with strong competitiveness in the global market. Excavators (8429.52) and loaders (8429.51), which have the largest export share of Korean construction machinery, have a weight advantage, but they are exporting due to price inferiority. The rest of the items were found to be inferior in price and weight, and were not competitive in the global market. These analysis results suggest the following implications. First, it is necessary to strengthen efforts to expand exports of universal construction machinery items, which are expected to increase in demand in the future, by boosting the economy and expanding infrastructure investment in accordance with eco-friendly policies. Second, excavators, which have been shown to have a quality advantage and a price competitive advantage, need to further strengthen export marketing activities not only in China and the United States but also in emerging developing countries.

• Interdisciplinary Bio Central
• /
• v.6 no.4
• /
• pp.3.1-3.10
• /
• 2014

Amino acid substitution matrices are essential tools for protein sequence analysis, homology sequence search in protein databases and multiple sequence alignment. The PAM matrix was the first widely used amino acid substitution matrix. The BLOSUM series then succeeded the PAM matrix. Most substitution matrixes were developed by using the statistical frequency of substitution between each amino acid at blocks representing groups of protein families or related proteins. However, substitution of amino acids is based on the similarity of physiochemical properties of each amino acid. In this study, a new approach was used to obtain major physiochemical properties in multiple sequence alignment. Frequency of amino acid substitution in multiple sequence alignment database and selected attributes of amino acids in physiochemical properties database were merged. This merged data showed the major physiochemical properties through principle components analysis. Using factor analysis, these four principle components were interpreted as flexibility of electronic movement, polarity, negative charge and structural flexibility. Applying these four components, BAPS was constructed and validated for accuracy. When comparing receiver operated characteristic ($ROC_{50}$) values, BAPS scored slightly lower than BLOSUM and PAM. However, when evaluating for accuracy by comparing results from multiple sequence alignment with the structural alignment results of two test data sets with known three-dimensional structure in the homologous structure alignment database, the result of the test for BAPS was comparatively equivalent or better than results for prior matrices including PAM, Gonnet, Identity and Genetic code matrix.

• IEIE Transactions on Smart Processing and Computing
• /
• v.4 no.5
• /
• pp.311-317
• /
• 2015

The aim of this study is to develop a 3D registration algorithm for positron emission tomography/computed tomography (PET/CT) and magnetic resonance (MR) images acquired from independent PET/CT and MR imaging systems. Combined PET/CT images provide anatomic and functional information, and MR images have high resolution for soft tissue. With the registration technique, the strengths of each modality image can be combined to achieve higher performance in diagnosis and radiotherapy planning. The proposed method consists of two stages: normalized mutual information (NMI)-based global matching and independent component analysis (ICA)-based refinement. In global matching, the field of view of the CT and MR images are adjusted to the same size in the preprocessing step. Then, the target image is geometrically transformed, and the similarities between the two images are measured with NMI. The optimization step updates the transformation parameters to efficiently find the best matched parameter set. In the refinement stage, ICA planes from the windowed image slices are extracted and the similarity between the images is measured to determine the transformation parameters of the control points. B-spline. based freeform deformation is performed for the geometric transformation. The results show good agreement between PET/CT and MR images.