• Smart Structures and Systems
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• v.6 no.5_6
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• Journal of Distribution Science
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• v.19 no.12
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• pp.33-43
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• 2021

Purpose: This paper aims to determine features of the relationship between human development, tourism and national brand. Research design, data and methodology: ranking indicators, cluster analysis, K means method, correlation analysis. Results: The analysis covers data for 95 countries for 2019. The number of countries is justified by the availability of comparable data for calculations. A direct relationship between the indicators for the entire sample has been revealed in the result of the correlation analysis. However, this relationship has not been confirmed for the groups of countries that were formed through the cluster analysis. Spearman Rank Order and Kendall Tau Correlations have been calculated for the five obtained clusters. In two of the five clusters, the relationship between the indicators has not been found. A strong negative link between all the indicators has been detected in the cluster with average index values. A strong positive link between TTCI and BSI has been revealed in the group of countries with the best index values. A strong positive link between TTCI and HDI has been found in the group of countries with the worst index values. Conclusions: The analysis demonstrates that there is a relationship between BSI, TTCI and HDI, and while this link is observed for the sample as a whole, it is not homogeneous for groups of countries.

• PNF and Movement
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• v.11 no.1
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• pp.27-33
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• 2013

Purpose : The purpose of this research was to research on Needs for the Construction of the CSFT(Cluster with a Strongpoint for Field Training) on students and professors of health-related majors. Methods : We investigated 164 students and professors using a self-reporting method with experience of Field Training. A statistical analysis was performed using SPSS 17.0 for window version. Results : It showed that educational satisfaction had scored 4.05 in curriculum, 4.00 in environment, 3.52 in schedule, 3.71 in evaluation and 3.71 in teaching and 3.84 in industrial-college systems for Field Training. Needs for the Construction of the CSFT had scored 4.17 in $mean{\pm}standard$ deviation. Conclusion : Characterization of Nursing, Department of Health and local health care environment and conditions, if you think the quality of education for the Department of Health Nursing, gradually, the acquisition and improvement of the base hospital is necessary. Therefore, it is considered to be institutionalized by installing the strongpoint hospital at least one in each region, so that they can contribute to the improvement of people's health.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.5
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• pp.891-898
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• 2022

Along with global interest, drones are expanding the base of utilization such as transportation of goods, forest protection, and safety management, and cluster flights are being applied in various fields such as military operations and environmental monitoring. Currently, specialized networks such as e-UM 5G for services in specific industries are being established in Korea. In this regard, drone systems are also moving to establish specialized networks to provide services that are fused with AI and autonomous flight. As drones converge with various services, various security threats in various environments are also subordinated, and in response, requirements and guidelines for drone security are being prepared in Korea. In this paper, we propose a technology method for peer identification and safe information provision between cluster flight drones by utilizing a cryptographic module equipped with wireless LAN and quantum entropy-based random number generator in a cluster flight system and a mobile communication network such as e-UM 5G.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.1-7
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• 2022

As the number of IoT devices increases, power management of the cluster head, which acts as a gateway between the cluster and sink nodes in the IoT network, becomes crucial. Particularly when the cluster head is a mobile wireless terminal, the power consumption of the IoT network must be minimized over its lifetime. In addition, the delay of information transmission in the IoT network is one of the primary metrics for rapid information collecting in the IoT network. In this paper, we propose a low-power buffer management algorithm that takes into account the information transmission delay in an IoT network. By forwarding or skipping received packets utilizing deep Q learning employed in deep reinforcement learning methods, the suggested method is able to reduce power consumption while decreasing transmission delay level. The proposed approach is demonstrated to reduce power consumption and to improve delay relative to the existing buffer management technique used as a comparison in slotted ALOHA protocol.

• The Journal of Bigdata
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• v.8 no.2
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• pp.45-54
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• 2023

Seoul's public bicycle system, 'Ttareungyi,' introduced in 2015, has achieved an annual ridership of 40 million in 2022. Similarly, electric scooters, a type of personal mobility device, surpassed one million riders in 2020 due to various sharing platforms. However, the major roadways for these new transportation, bicycle lanes, are notably insufficient compared to other forms of transport. Hence, this study proposes an optimal location selection method for bicycle lanes in Seoul to prevent accidents and enhance bicycle ride safety. The location selection process prioritizes road safety concerning bicycle accident risk. Using regression models, high-risk areas for bicycle accidents are identified. Cluster analysis categorizes these areas into six clusters, each suggesting suitable types of bicycle lanes based on cluster-specific characteristics. We hope that this study will contribute to the improvement of Seoul's transportation environment, including the expansion of dedicated bicycle lanes and lanes for personal mobility devices.

• Journal of The Korean Association For Science Education
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• v.40 no.1
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• pp.41-50
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• 2020

Looking at the understanding of scientific concepts from a linguistic perspective, it is very important for students to develop a deep and sophisticated understanding of words used in scientific concept as well as the ability to use them correctly. This study intends to provide the basis for productive knowledge education of scientific words by noting that the foundation of productive knowledge teaching on scientific words is not well established, and by exploring ways to teach the relationship among words that constitute scientific concept in a productive and effective manner. To this end, we extracted the relationship among the words that make up the scientific concept from the text of science textbook by using quantitative text analysis methods, second, qualitatively examined the meaning of the word relationship extracted as a result of each method, and third, we proposed a writing activity method to help improve the productive knowledge of scientific concept words. We analyzed the text of the "Force and motion" unit on first grade science textbook by using four methods of quantitative linguistic analysis: word cluster, co-occurrence, text network analysis, and word-embedding. As results, this study suggests four writing activities, completing sentence activity by using the result of word cluster analysis, filling the blanks activity by using the result of co-occurrence analysis, material-oriented writing activities by using the result of text network analysis, and finally we made a list of important words by using the result of word embedding.

• The KIPS Transactions:PartD
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• v.13D no.7 s.110
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• pp.857-866
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• 2006

Performance of an image retrieval system is usually very low because of the semantic gap between the low level feature and the high level concept in a query image. Semantically relevant images may exhibit very different visual characteristics, and may be scattered in several clusters. In this paper, we propose a content based image rertrieval approach which combines region based image retrieval and a new relevance feedback method using adaptive clustering together. Our main goal is finding semantically related clusters to narrow down the semantic gap. Our method consists of region based clustering processes and cluster-merging process. All segmented regions of relevant images are organized into semantically related hierarchical clusters, and clusters are merged by finding the number of the latent clusters. This method, in the cluster-merging process, applies r: using v principal components instead of classical Hotelling's $T_v^2$ [1] to find the unknown number of clusters and resolve the singularity problem in high dimensions and demonstrate that there is little difference between the performance of $T^2$ and that of $T_v^2$. Experiments have demonstrated that the proposed approach is effective in improving the performance of an image retrieval system.

• KIPS Transactions on Computer and Communication Systems
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• v.2 no.7
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• pp.283-292
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• 2013

In order to ensure high performance, all the servers in an existing server cluster are always On regardless of number of real-time requests. They ensure QoS, but waste server power if some of them are idle. To save energy consumed by servers, the server power mode control was developed by shutdowning a server when a server is not needed. There are two types of server power mode control depending on when a server is actually turned off if the server is selected to be off: static or dynamic. In a static mode, the server power is actually turned off after a fixed time delay from the time of the server selection. In a dynamic mode, server power is actually turned off if all the services served in the server are done. This corresponds to a turn off after a variable time delay. The static mdoe has disadvantages. It takes much time to find an optimal shutdown time manually through repeated experiments. In this paper, we propose a dynamic shutdown method to overcome the disadvantages of static shutdown. The proposed method allows to guarantee user QoS with good power-saving because it automatically approaches an optimal shutdown time. We performed experiments using 30 PCs cluster. Experimental results show that the proposed dynamic shutdown method is almost same as the best static shutdown in terms of power saving, but better than the best static shutdown in terms of QoS.

• Food Science of Animal Resources
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• v.40 no.5
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• pp.852-859
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• 2020

The muscle stem cells of domestic animals are of interest to researchers in the food and biotechnology industries for the production of cultured meat. For producing cultured meat, it is crucial for muscle stem cells to be efficiently isolated and stably maintained in vitro on a large scale. In the present study, we aimed to optimize the method for the enrichment of pig muscle stem cells using a magnetic-activated cell sorting (MACS) system. Pig muscle stem cells were collected from the biceps femoris muscles of 14 d-old pigs of three breeds [Landrace×Yorkshire×Duroc (LYD), Berkshire, and Korean native pigs] and cultured in skeletal muscle growth medium-2 (SkGM-2) supplemented with epidermal growth factor (EGF), dexamethasone, and a p38 inhibitor (SB203580). Approximately 30% of total cultured cells were nonmyogenic cells in the absence of purification in our system, as determined by immunostaining for cluster of differentiation 56 (CD56) and CD29, which are known markers of muscle stem cells. Interestingly, following MACS isolation using the CD29 antibody, the proportion of CD56⁺/CD29⁺ muscle stem cells was significantly increased (91.5±2.40%), and the proportion of CD56 single-positive nonmyogenic cells was dramatically decreased. Furthermore, we verified that this method worked well for purifying muscle stem cells in the three pig breeds. Accordingly, we found that CD29 is a valuable candidate among the various marker genes for the isolation of pig muscle stem cells and developed a simple sorting method based on a single antibody to this protein.