A Study on the Development of Ultrasonography Guide using Motion Tracking System (이미지 가이드 시스템 기반 초음파 검사 교육 기법 개발: 예비 연구)
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- Journal of the Korean Society of Radiology
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- v.17 no.7
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- pp.1067-1073
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- 2023
Breast cancer is one of the top three most common cancers in modern women, and the incidence rate is increasing rapidly. Breast cancer has a high family history and a mortality rate of about 15%, making it a high-risk group. Therefore, breast cancer needs constant management after an early examination. Among the various equipment that can diagnose cancer, ultrasound has the advantage of low risk and being able to diagnose in real time. In addition, breast ultrasound will be more useful because Asian women's breasts are denser and less sensitive. However, the results of ultrasound examinations vary greatly depending on the technology of the examiner. To compensate for this, we intend to incorporate motion tracking technology. Motion tracking is a technology that specifies and analyzes a location according to the movement of an object in a three-dimensional space. Therefore, real-time control is possible, and complex and fast movements can be recorded in real time. We would like to present the production of an ultrasound examination guide using these advantages.
The spatial and temporal non-uniform distribution of precipitation makes water management difficult. Due to climate change, nonuniform distribution of precipitation is worsening, and droughts and floods are occurring frequently. Additionally, the intensity of droughts and floods is intensifying, making existing water management systems difficult. From June 2022 to June 2023, most of the water storage rates of major dams in the Yeongsan river and Seomjin river basin were below 30%. In the case of Juam dam, which is the most dependent on water use in the basin, the water storage rate fell to 20.3%, the lowest ever. Pyeongnim dam recorded the lowest water storage rate of 27.3% on May 4, 2023. Due to a lack of precipitation starting in the spring of 2022, Pyeongnim dam was placed at a drought concern level on June 19, 2022, and entered the severe drought level on August 21. Pyeongrim dam and Suyangje(dam) have different operating institutions. Nevertheless, the low water level was not reached at Pyeongnim dam through organic linkage operation in a drought situation. Pyeongnim dam was able to stably supply water to 63,000 people in three counties. In order to maximize the use of limited water resources, we must review ways to move water smoothly between basins and water sources, and prepare for water shortages caused by climate change by establishing a consumer-centered water supply system.
The anorthositic rocks of the study area are divided into the northern Sancheong and southern Hadong anorthositic rocks depending on the different distribution patterns and lithologies. In order to evaluate the characteristics of the hydrothermal systems developed in the study area, oxygen and hydrogen isotopic compositions of the anorthositic rocks were measured. Oxygen isotopic values of the plagioclase exhibit an interesting spatial distribution. Plagioclase collected from the Sancheong anorthositic rocks in the northern part tends to have a relatively restricted range of
Both Platform as a Service (PaaS) as one of the cloud computing service models and the e-government (e-Gov) standard framework from the Ministry of the Interior and Safety (MOIS) provide developers with practical computing environments to build their applications in every web-based services. Web application developers in the geo-spatial information field can utilize and deploy many middleware software or common functions provided by either the cloud-based service or the e-Gov standard framework. However, there are few studies for their applicability and performance in the field of actual geo-spatial information application yet. Therefore, the motivation of this study was to investigate the relevance of these technologies or platform. The applicability of these computing environments and the performance evaluation were performed after a test application deployment of the spatial image processing case service using Web Processing Service (WPS) 2.0 on the e-Gov standard framework. This system was a test service supported by a cloud environment of Cloud Foundry, one of open source PaaS cloud platforms. Using these components, the performance of the test system in two cases of 300 and 500 threads was assessed through a comparison test with two kinds of service: a service case for only the PaaS and that on the e-Gov on the PaaS. The performance measurements were based on the recording of response time with respect to users' requests during 3,600 seconds. According to the experimental results, all the test cases of the e-Gov on PaaS considered showed a greater performance. It is expected that the e-Gov standard framework on the PaaS cloud would be important factors to build the web-based spatial information service, especially in public sectors.
Recommender system has become one of the most important technologies in e-commerce in these days. The ultimate reason to shop online, for many consumers, is to reduce the efforts for information search and purchase. Recommender system is a key technology to serve these needs. Many of the past studies about recommender systems have been devoted to developing and improving recommendation algorithms and collaborative filtering (CF) is known to be the most successful one. Despite its success, however, CF has several shortcomings such as cold-start, sparsity, gray sheep problems. In order to be able to generate recommendations, ordinary CF algorithms require evaluations or preference information directly from users. For new users who do not have any evaluations or preference information, therefore, CF cannot come up with recommendations (Cold-star problem). As the numbers of products and customers increase, the scale of the data increases exponentially and most of the data cells are empty. This sparse dataset makes computation for recommendation extremely hard (Sparsity problem). Since CF is based on the assumption that there are groups of users sharing common preferences or tastes, CF becomes inaccurate if there are many users with rare and unique tastes (Gray sheep problem). This study proposes a new algorithm that utilizes Social Network Analysis (SNA) techniques to resolve the gray sheep problem. We utilize 'degree centrality' in SNA to identify users with unique preferences (gray sheep). Degree centrality in SNA refers to the number of direct links to and from a node. In a network of users who are connected through common preferences or tastes, those with unique tastes have fewer links to other users (nodes) and they are isolated from other users. Therefore, gray sheep can be identified by calculating degree centrality of each node. We divide the dataset into two, gray sheep and others, based on the degree centrality of the users. Then, different similarity measures and recommendation methods are applied to these two datasets. More detail algorithm is as follows: Step 1: Convert the initial data which is a two-mode network (user to item) into an one-mode network (user to user). Step 2: Calculate degree centrality of each node and separate those nodes having degree centrality values lower than the pre-set threshold. The threshold value is determined by simulations such that the accuracy of CF for the remaining dataset is maximized. Step 3: Ordinary CF algorithm is applied to the remaining dataset. Step 4: Since the separated dataset consist of users with unique tastes, an ordinary CF algorithm cannot generate recommendations for them. A 'popular item' method is used to generate recommendations for these users. The F measures of the two datasets are weighted by the numbers of nodes and summed to be used as the final performance metric. In order to test performance improvement by this new algorithm, an empirical study was conducted using a publically available dataset - the MovieLens data by GroupLens research team. We used 100,000 evaluations by 943 users on 1,682 movies. The proposed algorithm was compared with an ordinary CF algorithm utilizing 'Best-N-neighbors' and 'Cosine' similarity method. The empirical results show that F measure was improved about 11% on average when the proposed algorithm was used