• Journal of KIISE:Information Networking
• /
• v.30 no.3
• /
• pp.329-342
• /
• 2003

Most of the existing multicast routing protocols for ad-hoc networks do not take into account the efficiency of the protocol for the cases when there are large number of sources in the multicast group, resulting in either large overhead or poor data delivery ratio when the number of sources is large. In this paper, we propose a multicast routing protocol for ad-hoc networks, which particularly considers the scalability of the protocol in terms of the number of sources in the multicast groups. The proposed protocol designates a set of sources as the core sources. Each core source is a root of each tree that reaches all the destinations of the multicast group. The union of these trees constitutes the data delivery mesh, and each of the non-core sources finds the nearest core source in order to delegate its data delivery. For the efficient operation of the proposed protocol, it is important to have an appropriate number of core sources. Having too many of the core sources incurs excessive control and data packet overhead, whereas having too little of them results in a vulnerable and overloaded data delivery mesh. The data delivery mesh is optimally reconfigured through the periodic control message flooding from the core sources, whereas the connectivity of the mesh is maintained by a persistent local mesh recovery mechanism. The simulation results show that the proposed protocol achieves an efficient multicast communication with high data delivery ratio and low communication overhead compared with the other existing multicast routing protocols when there are multiple sources in the multicast group.

• Maritime Security
• /
• v.1 no.1
• /
• pp.215-240
• /
• 2020

In 2017, the U.S. DARPA coined 'mosaic warfare' as a new way of warfighting. According to the Timothy Grayson, director of DARPA's Strategic Technologies Office, mosaic warfare is a "system of system" approach to warfghting designed around compatible "tiles" of capabilities, rather than uniquely shaped "puzzle pieces" that must be fitted into a specific slot in a battle plan in order for it to work. Prior to cover mosaic warfare theory and recent development, it deals analyze its background and several premises for better understanding. The U.S. DoD officials might acknowledge the current its forces vulnerability to the China's A2/AD assets. Furthermore, the U.S. seeks to complete military superiority even in other nation's territorial domains including sea and air. Given its rapid combat restoration capability and less manpower casualty, the U.S. would be able to ready to endure war of attrition that requires massive resources. The core concept of mosaic warfare is a "decision centric warfare". To embody this idea, it create adaptability for U.S. forces and complexity or uncertainty for the enemy through the rapid composition and recomposition of a more disag g reg ated U.S. military force using human command and machine control. This allows providing more options to friendly forces and collapse adversary's OODA loop eventually. Adaptable kill web, composable force packages, A.I., and context-centric C3 architecture are crucial elements to implement and carry out mosaic warfare. Recently, CSBA showed an compelling assessment of mosaic warfare simulation. In this wargame, there was a significant differences between traditional and mosaic teams. Mosaic team was able to mount more simultaneous actions, creating additional complexity to adversaries and overwhelming their decision-making with less friendly force's human casualty. It increase the speed of the U.S. force's decision-making, enabling commanders to better employ tempo. Consequently, this article finds out and suggests implications for Korea armed forces. First of all, it needs to examine and develop 'mosaic warfare' in terms of our security circumstance. In response to future warfare, reviewing overall force structure and architecture is required which is able to compose force element regardless domain. In regards to insufficient defense resources and budget, "choice" and "concentration" are also essential. It needs to have eyes on the neighboring countries' development of future war concept carefully.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.51 no.3
• /
• pp.70-82
• /
• 2023

The recent surge of IT and data acquisition is shifting the paradigm in all aspects of life, and these advances are also affecting academic fields. Research topics and methods are being improved through academic exchange and connections. In particular, data-based research methods are employed in various academic fields, including landscape architecture, where continuous research is needed. Therefore, this study aims to investigate the possibility of developing a landscape preference evaluation and prediction model using machine learning, a branch of Artificial Intelligence, reflecting the current situation. To achieve the goal of this study, machine learning techniques were applied to the landscaping field to build a landscape preference evaluation and prediction model to verify the simulation accuracy of the model. For this, wind power facility landscape images, recently attracting attention as a renewable energy source, were selected as the research objects. For analysis, images of the wind power facility landscapes were collected using web crawling techniques, and an analysis dataset was built. Orange version 3.33, a program from the University of Ljubljana was used for machine learning analysis to derive a prediction model with excellent performance. IA model that integrates the evaluation criteria of machine learning and a separate model structure for the evaluation criteria were used to generate a model using kNN, SVM, Random Forest, Logistic Regression, and Neural Network algorithms suitable for machine learning classification models. The performance evaluation of the generated models was conducted to derive the most suitable prediction model. The prediction model derived in this study separately evaluates three evaluation criteria, including classification by type of landscape, classification by distance between landscape and target, and classification by preference, and then synthesizes and predicts results. As a result of the study, a prediction model with a high accuracy of 0.986 for the evaluation criterion according to the type of landscape, 0.973 for the evaluation criterion according to the distance, and 0.952 for the evaluation criterion according to the preference was developed, and it can be seen that the verification process through the evaluation of data prediction results exceeds the required performance value of the model. As an experimental attempt to investigate the possibility of developing a prediction model using machine learning in landscape-related research, this study was able to confirm the possibility of creating a high-performance prediction model by building a data set through the collection and refinement of image data and subsequently utilizing it in landscape-related research fields. Based on the results, implications, and limitations of this study, it is believed that it is possible to develop various types of landscape prediction models, including wind power facility natural, and cultural landscapes. Machine learning techniques can be more useful and valuable in the field of landscape architecture by exploring and applying research methods appropriate to the topic, reducing the time of data classification through the study of a model that classifies images according to landscape types or analyzing the importance of landscape planning factors through the analysis of landscape prediction factors using machine learning.