• Journal of Pharmacopuncture
• /
• v.18 no.3
• /
• pp.75-79
• /
• 2015

Objectives: As the peripheral part of the primo vascular system (PVS) is difficult to visualize, we used a vascular casting material Mercox injected directly into the skin to take advantage of a simple procedure to visualize PVS structures as primo vessels (PVs) and primo nodes (PNs) in the skin. Methods: Two colors of the polymer Mercox were injected into mouse skin. After a partial maceration of the whole body with potassium hydroperoxide solution, we anatomized it under a stereomicroscope to trace the Mercox that had been injected into the PVS. Results: Injection of Mercox directly into the skin allowed the PVs and the PNs to be visualized. This approach can fill the PVS when the material is ejected out of the PVs or PNs. The shapes, sizes, and topographic positions of the nodes and the vessels are the hallmarks used to identify the PVS in skin when Mercox is used as a tracer. Conclusion: The direct injection of the casting material Mercox into skin, with modified partial maceration procedures, is a promising method for visualizing the PVs and the PNs in the peripheral part of the PVS in skin. The polymer Mercox can penetrate through the primo pores of the primo vascular wall and fill the PVs and the PNs. The data prove that PVs and PNs exist on the hypodermal layer of the skin.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2004.11a
• /
• pp.830-833
• /
• 2004

An indoor package air-conditioner (PAC) has complex noise sources such as motor noise and fluid noise caused by the fan motor, heat transfer and shroud. Sound intensity techniques and ODS(Operational deflection shape) techniques are applied to identify the noise characteristics of an indoor air-conditioner's cabinet. The sound intensity is used to visualize the noise source locations. and the ODS to visualize the vibration pattern and to obtain the dynamic characteristics of the noise source. Acoustic intensity and operational deflection distribution are obtained in space domains as well as frequency domains. Using the visual information of source locations and its dynamic characteristics, the damping patch is applied to reduce structure borne noise in the cabinet. As a result, the noise emitted by the cabinet is reduced by 5dB.

• Analytical Science and Technology
• /
• v.32 no.3
• /
• pp.105-112
• /
• 2019

Fingerprints may be contaminated with ethanol solutions. In order to solve the case, the law enforcement agency may need to visualize the fingerprint from these samples, but the development method has not been studied. The paper with latent fingerprint was contaminated with ethanol solution and then the blurring of ridge detail was observed. As a result, when the copy paper was contaminated with ethanol solutions of less than 75 % (v/v), the amino acid components of latent fingerprint residue blurred but lipid components of latent fingerprint residue didn't blurred. On the other hand, when the paper was contaminated with ethanol solution of more than 80 % (v/v), the amino acid components of latent fingerprint didn't blurred but the lipid components of latent fingerprint blurred. Therefore, it is found that the paper contaminated with ethanol solutions of less than 75 % (v/v) should be treated by oil red O (ORO) enhancing lipid components, and the paper contaminated with ethanol solutions of 80 % (v/v) or more should be treated by 1,2-indandione/zinc (1,2-IND/Zn) enhancing amino acid components. The blurring of ridge detail was not observed when the fingerprints were deposited with fingers contaminated with ethanol solution. This fingerprints were treated with 1,2-IND/Zn or ORO to compare the latent fingerprint development ability, and using 1,2-IND/Zn was able to visualize the latent fingerprint more clearly than using ORO.

• Journal of Science Education
• /
• v.43 no.1
• /
• pp.173-193
• /
• 2019

An expert teachers' instruction can be helpful to other teachers because good teaching effectively guides students to develop meaningful learning. Feynman is an excellent physics lecturer as well as one of the greatest physicists of the 20th century who presented and explained physics with his unique teaching style based on his great store of knowledge. However, it is not easy to capture and visualize teaching because it is not only the complex phenomena interrelated to various factors with the content to be taught but also the tacit representation. In this study, the framework of knowledge & belief based on the integrated mental model theory was used as a tool to capture and visualize complex and tacit representation of Feynman's teaching of 'The theory of gravitation,' a chapter in The Feynman Lectures on Physics. Feynman's teaching was found to go beyond the transmission of physics concepts by showing that components of the framework of knowledge & belief were effectively intertwined and integrated in his teaching and the storyline was well-organized. On the basis of these discussions, the implications of Feynman's teaching analyzed within the framework of knowledge & belief for physics teacher education are derived. Finally, the characteristics of the framework of knowledge & belief as tools for the analysis of teaching are presented.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.14 no.5
• /
• pp.69-78
• /
• 2004

The change of attack techniques paradigm was begun by fast extension of the latest Internet and new attack form appearing. But, Most intrusion detection systems detect only known attack type as IDS is doing based on misuse detection, and active correspondence is difficult in new attack. Therefore, to heighten detection rate for new attack pattern, the experiments to apply various techniques of anomaly detection are appearing. In this paper, we propose an behavior profiling method using Bayesian framework based on graphics from audit data and visualize behavior profile to detect/analyze anomaly behavior. We achieve simulation to translate host/network audit data into BF-XML which is behavior profile of semi-structured data type for anomaly detection and to visualize BF-XML as SVG.

• Journal of KIBIM
• /
• v.11 no.2
• /
• pp.54-59
• /
• 2021

In the process of designing a building, the process of expressing the designer's ideas through images is essential. However, it is expensive and time consuming for a designer to analyze every individual case image to generate a hypothetical design. This study aims to visualize the basic design draft sketch made by the designer as a real image using the Generative Adversarial Network (GAN) based on the continuously accumulated architectural case images. Through this, we proposed a method to build an automated visualization environment using artificial intelligence and to visualize the architectural idea conceived by the designer in the architectural planning stage faster and cheaper than in the past. This study was conducted using approximately 20,000 images. In our study, the GAN algorithm allowed us to represent primary materials and shades within 2 seconds, but lacked accuracy in material and shading representation. We plan to add image data in the future to address this in a follow-up study.

• Imaging Science in Dentistry
• /
• v.52 no.2
• /
• pp.225-230
• /
• 2022

Purpose: This report presents the first known use of a rigid endoscope with augmented reality technology for the removal of an odontogenic cyst that penetrated the maxillary sinus and illustrates its practical use in a patient. Materials and Methods: In the preoperative period, cone-beam computed tomography was performed in a specially designed marker holder frame, and the contours of the cyst and the nearest anatomical formations were segmented in the 3D Slicer program. During the operation, a marker was installed on the patient's head, as well as on the tip of the endoscope, which made it possible to visualize the mass and the movement of the endoscope. The surgical intervention was performed with the support of augmented reality in HoloLens glasses (Microsoft Corporation, Redmond, WA, USA). Results: The use of this technology improved the accuracy of surgical manipulations, reduced operational risks, and shortened the time of surgery and the rehabilitation period. Conclusion: With the help of modern technologies, a navigation system was created that helped to track the position of the endoscope in mixed reality in real time, as well as to fully visualize anatomical formations.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.35 no.5
• /
• pp.509-515
• /
• 2022

As the 4th industrial revolution based on ICT is progressing in the manufacturing field, interest in building smart factories that can be flexible and customized according to customer demand is increasing. To this end, it is necessary to maximize the efficiency of factory by performing an automated process in real time through a network communication between engineers and equipment to be able to link the established IT system. It is also necessary to collect and store real-time data from heterogeneous facilities and to analyze and visualize a vast amount of data to utilize necessary information. Therefore, in this study, four types of controllers such as PLC, Arduino, Raspberry Pi, and embedded system, which are generally used to build a smart factory that can connect technologies such as artificial intelligence (AI), Internet of Things (IoT), and big data, are configured. This study was conducted for the development of a program that can collect and store data in real time to visualize and manage information. For communication verification by controller, data communication was implemented and verified with the data log in the program, and 3D monitoring was implemented and verified to check the process status such as planned quantity for each controller, actual quantity, production progress, operation rate, and defect rate.

• Journal of Korea Multimedia Society
• /
• v.25 no.2
• /
• pp.363-373
• /
• 2022

Multi-omics data is difficult to interpret due to the heterogeneity of information by the volume of data, the complexity of characteristics of each data, and the diversity of omics platforms. There is not yet a system for interpreting to visualize research data on environmental diseases concerning environmental harmful substances. We provide MEE, a web-based visualization tool, to comprehensively explore the complexity of data due to the interconnected characteristics of high-dimensional data sets according to exposure to various environmental harmful substances. MEE visualizes omics data of correlation between omics data, subjects and samples by keyword searches of meta data, multi-omics data, and harmful substances. MEE has been demonstrated the versatility by two examples. We confirmed the correlation between smoking and asthma with RNA-seq and Methylation-Chip data, it was visualized that genes (P HACTR3, PXDN, QZMB, SOCS3 etc.) significantly related to autoimmune or inflammatory diseases. To visualize the correlation between atopic dermatitis and heavy metals, we selected 32 genes related immune response by integrated analysis of multi-omics data. However, it did not show a significant correlation between mercury in blood and atopic dermatitis. In the future, should continuously collect an appropriate level of multi-omics data in MEE system, will obtain data to analyze environmental substances and diseases.

• Journal of Intelligence and Information Systems
• /
• v.28 no.1
• /
• pp.131-154
• /
• 2022

Recently, researches on unstructured data analysis have been actively conducted with the development of information and communication technology. In particular, topic modeling is a representative technique for discovering core topics from massive text data. In the early stages of topic modeling, most studies focused only on topic discovery. As the topic modeling field matured, studies on the change of the topic according to the change of time began to be carried out. Accordingly, interest in dynamic topic modeling that handle changes in keywords constituting the topic is also increasing. Dynamic topic modeling identifies major topics from the data of the initial period and manages the change and flow of topics in a way that utilizes topic information of the previous period to derive further topics in subsequent periods. However, it is very difficult to understand and interpret the results of dynamic topic modeling. The results of traditional dynamic topic modeling simply reveal changes in keywords and their rankings. However, this information is insufficient to represent how the meaning of the topic has changed. Therefore, in this study, we propose a method to visualize topics by period by reflecting the meaning of keywords in each topic. In addition, we propose a method that can intuitively interpret changes in topics and relationships between or among topics. The detailed method of visualizing topics by period is as follows. In the first step, dynamic topic modeling is implemented to derive the top keywords of each period and their weight from text data. In the second step, we derive vectors of top keywords of each topic from the pre-trained word embedding model. Then, we perform dimension reduction for the extracted vectors. Then, we formulate a semantic vector of each topic by calculating weight sum of keywords in each vector using topic weight of each keyword. In the third step, we visualize the semantic vector of each topic using matplotlib, and analyze the relationship between or among the topics based on the visualized result. The change of topic can be interpreted in the following manners. From the result of dynamic topic modeling, we identify rising top 5 keywords and descending top 5 keywords for each period to show the change of the topic. Existing many topic visualization studies usually visualize keywords of each topic, but our approach proposed in this study differs from previous studies in that it attempts to visualize each topic itself. To evaluate the practical applicability of the proposed methodology, we performed an experiment on 1,847 abstracts of artificial intelligence-related papers. The experiment was performed by dividing abstracts of artificial intelligence-related papers into three periods (2016-2017, 2018-2019, 2020-2021). We selected seven topics based on the consistency score, and utilized the pre-trained word embedding model of Word2vec trained with 'Wikipedia', an Internet encyclopedia. Based on the proposed methodology, we generated a semantic vector for each topic. Through this, by reflecting the meaning of keywords, we visualized and interpreted the themes by period. Through these experiments, we confirmed that the rising and descending of the topic weight of a keyword can be usefully used to interpret the semantic change of the corresponding topic and to grasp the relationship among topics. In this study, to overcome the limitations of dynamic topic modeling results, we used word embedding and dimension reduction techniques to visualize topics by era. The results of this study are meaningful in that they broadened the scope of topic understanding through the visualization of dynamic topic modeling results. In addition, the academic contribution can be acknowledged in that it laid the foundation for follow-up studies using various word embeddings and dimensionality reduction techniques to improve the performance of the proposed methodology.