• Journal of the Korea Society of Computer and Information
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• v.21 no.5
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• pp.19-29
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• 2016

This paper suggests a method to refine a massive collective intelligence data, and visualize with multilevel sentiment network, in order to understand information in an intuitive and semantic way. For this study, we first calculated a frequency of sentiment words from each movie review. Second, we designed a Heatmap visualization to effectively discover the main emotions on each online movie review. Third, we formed a Sentiment-Movie Network combining the MDS Map and Social Network in order to fix the movie network topology, while creating a network graph to enable the clustering of similar nodes. Finally, we evaluated our progress to verify if it is actually helpful to improve user cognition for multilevel analysis experience compared to the existing network system, thus concluded that our method provides improved user experience in terms of cognition, being appropriate as an alternative method for semantic understanding.

• Journal of Korea Multimedia Society
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• v.23 no.9
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• pp.1210-1218
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• 2020

This study selected brand logos of six South Korean and Chinese banks including KB, IBK, SH, ICBC, ABC, and SISB, conducted Eye Tracking experiment among 36 South Korean and Chinese university students(Nine male and female students, respectively), and analyzed the difference of visual attention of consumers on bank brand logo, symbol, Korean/Chinese character logo types as well as the difference of visual attention of these consumers on English logo types. Results were represented by using statistics and visualization including GAZEPLOT, HEATMAP, and visual expression. Results showed that most generally gazed logo types more often and longer than symbols when they watched bank brand logos. A slight difference was observed between both groups in terms of gazing English logo types. This study has a implication that it proposed the possibility of drawing quantitative and reliable outcomes by utilizing eye tracking device and approaching in an objective standpoint beyond a methodological aspect on bank brand logo primarily leaning over the analysis of case research or design development. Moreover, findings are expected to serve as basic data for proposing the direction of special bank brand logo design and marketing strategies.

• Proceedings of the Korean Society of Computer Information Conference
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• 2018.07a
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• pp.51-52
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• 2018

화력발전용 순환유동층 보일러는 환경오염의 주요인인 질소산화물(NOx)과 황산화물(SOx)의 배출량이 적은 친환경 화력발전용 보일러로 화력발전 업계에서 각광받고 있는 추세이다. 그러나 순환유동층 보일러의 연료인 유동매체는 미분탄과 같이 작지만 단단한 고체이므로 유동매체의 타격으로 인해 워터월(waterwall) 튜브의 마모는 물론 누설까지 야기할 수 있다. 순환유동층 보일러 튜브에서 누설된 증기는 보일러 내부에 클링커(Clinker)를 발생시키고 이는 순환유동층 보일러 튜브 표면에 응고되어 열전도율을 감소시킬 뿐만 아니라 보일러 운전정지의 원인이 된다. 따라서 본 논문에서는 음향방출 센서를 이용하여 화력발전용 순환유동층 보일러 튜브의 누설 위치를 추정하는 방법을 제안한다. 제안 방법에서는 매질의 분자단위 이동에 의해 발생되는 탄성파를 감지할 수 있는 음향방출 센서를 이용하고, 보일러 워터월 튜브의 멤브레인 용접부와 비용접부(seamless)의 감쇠율을 고려한 위치별 센서 감도 추정 알고리즘을 통해 워터월 튜브의 위치별 진폭 크기를 히트맵으로 표현할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.6
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• pp.3182-3198
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• 2019

Vision-based Human Pose Estimation has been considered as one of challenging research subjects due to problems including confounding background clutter, diversity of human appearances and illumination changes in scenes. To tackle these problems, we propose to use a new multi-stage convolution machine for estimating human pose. To provide better heatmap prediction of body joints, the proposed machine repeatedly produces multiple predictions according to stages with receptive field large enough for learning the long-range spatial relationship. And stages are composed of various modules according to their strategic purposes. Pyramid stacking module and dilation module are used to handle problem of human pose at multiple scales. Their multi-scale information from different receptive fields are fused with concatenation, which can catch more contextual information from different features. And spatial and channel information of a given input are converted to gating factors by squeezing the feature maps to a single numeric value based on its importance in order to give each of the network channels different weights. Compared with other ConvNet-based architectures, we demonstrated that our proposed architecture achieved higher accuracy on experiments using standard benchmarks of LSP and MPII pose datasets.

• Biomolecules & Therapeutics
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• v.30 no.1
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• pp.98-116
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• 2022

The small GTPase RhoA has been studied extensively for its role in actin dynamics. In this study, multiple bioinformatics tools were applied cooperatively to the microarray dataset GSE64714 to explore previously unidentified functions of RhoA. Comparative gene expression analysis revealed 545 differentially expressed genes in RhoA-null cells versus controls. Gene set enrichment analysis (GSEA) was conducted with three gene set collections: (1) the hallmark, (2) the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and (3) the Gene Ontology Biological Process. GSEA results showed that RhoA is related strongly to diverse pathways: cell cycle/growth, DNA repair, metabolism, keratinization, response to fungus, and vesicular transport. These functions were verified by heatmap analysis, KEGG pathway diagramming, and direct acyclic graphing. The use of multiple gene set collections restricted the leakage of information extracted. However, gene sets from individual collections are heterogenous in gene element composition, number, and the contextual meaning embraced in names. Indeed, there was a limit to deriving functions with high accuracy and reliability simply from gene set names. The comparison of multiple gene set collections showed that although the gene sets had similar names, the gene elements were extremely heterogeneous. Thus, the type of collection chosen and the analytical context influence the interpretation of GSEA results. Nonetheless, the analyses of multiple collections made it possible to derive robust and consistent function identifications. This study confirmed several well-described roles of RhoA and revealed less explored functions, suggesting future research directions.

• Biomedical Science Letters
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• v.29 no.1
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• pp.1-10
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• 2023

Since its first identification in 1995, klotho (KL) has become the most promising gene to consider for suppressing aging and aging-related diseases. KL knockout mice exhibited similar phenotypes found in human with premature aging such as short lifespan, osteoporosis, arteriosclerosis and hearing loss. Genetically modified mice overexpressing KL prolonged lifespan more than 20%. Also, clinical reports have indicated decreased concentration of the circulating KL protein in blood, which is called soluble klotho (sKL), is closely related to development of senile diseases. The best way to discover significance of sKL on the development of the diseases might be comparison of sKL concentration between controls and patients. Here we analyzed published clinical reports identified sKL concentration in the cohorts. The sKL concentrations were displayed using heatmap for better comparison. In most of the senile diseases, disease progression was inversely related with sKL concentration. Hypertension was the only disease had no relationship, while schizophrenia was the only disease had direct proportion to the disease progression. Overall, sKL concentration in blood could be a marker to determine current severity of the senile diseases and even to estimate disease progression for the patients at the onset of their senile diseases.

• Journal of Distribution Science
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• v.20 no.6
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• pp.1-10
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• 2022

Purpose: This research proposes a new modified Recency-Frequency-Monetary (RFM) model by extending the model with spatial analysis for supporting decision-makers in discovering the promotional target market. Research design, data and methodology: This quantitative research utilizes data-mining techniques and the RFM model to cluster a university's provider schools. The RFM model was modified by adapting its variables to the university's marketing context and adding a district's potential (D) variable based on heatmap analysis using Geographic Information System (GIS) and K-means clustering. The K-prototype algorithm and the Elbow method were applied to find provider school clusters using the proposed RFM-D model. After profiling the clusters, the target segment was assigned. The model was validated using empirical data from an Indonesian university, and its performance was compared to the Customer Lifetime Value (CLV)-based RFM utilizing accuracy, precision, recall, and F1-score metrics. Results: This research identified five clusters. The target segment was chosen from the highest-value and high-value clusters that comprised 17.80% of provider schools but can contribute 75.77% of students. Conclusions: The proposed model recommended more targeted schools in higher-potential districts and predicted the target segment with 0.99 accuracies, outperforming the CLV-based model. The empirical findings help university management determine the promotion location and allocate resources for promotional information distribution and student recruitment.

• Annual Conference of KIPS
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• 2022.11a
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• pp.541-543
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• 2022

In recent years, retinal disorders have become a serious health concern. Retinal disorders develop slowly and without obvious signs. To avoid vision deterioration, early detection and treatment are critical. Optical coherence tomography (OCT) is a non-invasive and non-contact medical imaging technique used to acquire informative and high-resolution image of retinal area and underlying layers. Disease signs are difficult to detect because OCT images have many areas which are not related to any disease. In this paper, we present a deep learning-based method to perform multi-label classification on a long-tailed OCT dataset. Our method first extracts the region of interest and then performs the classification task. We achieve 98% accuracy, 92% sensitivity, and 99% specificity on our private OCT dataset. Using the heatmap generated from trained convolutional neural network, our method is more robust and explainable than previous approaches because it focuses on areas that contain disease signs.

• Korean Journal of Radiology
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• v.24 no.8
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• pp.752-760
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• 2023

Radiologists and trauma surgeons should monitor for early killers among patients with thoracic trauma, such as tension pneumothorax, tracheobronchial injuries, flail chest, aortic injury, mediastinal hematomas, and severe pulmonary parenchymal injury. With the advent of cutting-edge technology, rapid volumetric computed tomography of the chest has become the most definitive diagnostic tool for establishing or excluding thoracic trauma. With the notion of "time is life" at emergency settings, radiologists must find ways to shorten the turnaround time of reports. One way to interpret chest findings is to use a systemic approach, as advocated in this study. Our interpretation of chest findings for thoracic trauma follows the acronym "ABC-Please" in which "A" stands for abnormal air, "B" stands for abnormal bones, "C" stands for abnormal cardiovascular system, and "P" in "Please" stands for abnormal pulmonary parenchyma and vessels. In the future, utilizing an artificial intelligence software can be an alternative, which can highlight significant findings as "warm zones" on the heatmap and can re-prioritize important examinations at the top of the reading list for radiologists to expedite the final reports.

• Food Science of Animal Resources
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• v.44 no.4
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• pp.934-950
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• 2024

This study addresses the prevalent issue of meat species authentication and adulteration through a chemometrics-based approach, crucial for upholding public health and ensuring a fair marketplace. Volatile compounds were extracted and analyzed using headspace-solid-phase-microextraction-gas chromatography-mass spectrometry. Adulterated meat samples were effectively identified through principal component analysis (PCA) and partial least square-discriminant analysis (PLS-DA). Through variable importance in projection scores and a Random Forest test, 11 key compounds, including nonanal, octanal, hexadecanal, benzaldehyde, 1-octanol, hexanoic acid, heptanoic acid, octanoic acid, and 2-acetylpyrrole for beef, and hexanal and 1-octen-3-ol for pork, were robustly identified as biomarkers. These compounds exhibited a discernible trend in adulterated samples based on adulteration ratios, evident in a heatmap. Notably, lipid degradation compounds strongly influenced meat discrimination. PCA and PLS-DA yielded significant sample separation, with the first two components capturing 80% and 72.1% of total variance, respectively. This technique could be a reliable method for detecting meat adulteration in cooked meat.