• STI Policy Review
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• v.6 no.1
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• pp.1-23
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• 2015

Based on the work of Anand et al. (2013) we measure inclusive income growth, which combines growth in gross domestic product (GDP) per capita and growth in the equity of the income distribution. Extending the work of Causa et al. (2014), we estimate a dynamic simultaneous structural equations model of GDP per capita and inclusive income on panel data for 63 countries over the 1990-2013 period. We estimate both equations in error correction form by difference GMM (generalized method of moments). Among the explanatory variables of the level and the distribution of GDP per capita we include R&D (research and development) expenditure per capita. In OECD countries we obtain a large positive effect of R&D on GDP. R&D is found to have a positive effect on the social mobility index but its impact on the income equity index at first decreases, then switches around to become slightly positive in the long run. In non- OECD countries, R&D is found to decrease inclusive income, mostly through a negative growth effect but also because of a slightly increasing income inequity effect.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.105-107
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• 2015

In this paper, we proposed LTD(Load Tolerance Density-distribution) algorithm using dynamic density for analyzing distribute routing path. MANET(Mobile Ad-hoc Networks) consists of the node that has a mobility. By the Mobility, the topology is exchanged frequently. To reduce the exchange of topology, the hierarchy network is studied. However, if the load is concentrated at the cluster head node, the communication is disconnected. the proposed algorithm measure the dynamic density of the node using poisson distribution. And this algorithm provides distribute routing path using dynamic density. The simulation results, the proposed algorithm shows improved packet delivery ratio than the compared algorithm.

• Journal of the Korea Society of Computer and Information
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• v.11 no.4 s.42
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• pp.165-174
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• 2006

As context aware service supports a process of context acquisition and reasoning, there are many studies to facilitate the implementation of context aware service. However, these studies have not supported efficiently a user or service mobility that should be supported necessarily in ubiquitous computing environment. Therefore, this study proposes a dynamic context aware service model which supports a dynamic management of context information, service retrieval and composition for interactions between services, and service mobility. Then we design a middleware based on this model and implement the middleware. As the middleware is implemented on the OSGi framework. it can have an interoperability between the devices such as computers, PDA, home appliances, and sensors, because of using the standard interface technologies like UPnP. Jini and so on.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.2
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• pp.107-114
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• 2024

Mobile ad hoc networks represent self-configuring networks of mobile devices that communicate without relying on a fixed infrastructure. However, traditional routing protocols in such networks encounter challenges in selecting efficient and reliable routes due to dynamic nature of these networks caused by unpredictable mobility of nodes. This often results in a failure to meet the low-delay and low-energy consumption requirements crucial for such networks. In order to overcome such challenges, our paper introduces a novel multi-objective and adaptive routing scheme based on the Q-learning reinforcement learning algorithm. The proposed routing scheme dynamically adjusts itself based on measured network states, such as traffic congestion and mobility. The proposed approach utilizes Q-learning to select routes in a decentralized manner, considering factors like energy consumption, load balancing, and the selection of stable links. We present a formulation of the multi-objective optimization problem and discuss adaptive adjustments of the Q-learning parameters to handle the dynamic nature of the network. To speed up the learning process, our scheme incorporates informative shaped rewards, providing additional guidance to the learning agents for better solutions. Implemented on the widely-used AODV routing protocol, our proposed approaches demonstrate better performance in terms of energy efficiency and improved message delivery delay, even in highly dynamic network environments, when compared to the traditional AODV. These findings show the potential of leveraging reinforcement learning for efficient routing in ad hoc networks, making the way for future advancements in the field of mobile ad hoc networking.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.5
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• pp.654-666
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• 2019

The tactical network has several different characteristics compared with commercial internet network such as hierarchical topology, dynamic topology changing and wireless link based connectivity. For considering IP mobility management in the tactical network, current mobility management using Mobile IP(MIP) is not suitable with some reasons such as non-optimal routing paths and single point of failure. Proxy Mobile IP(PMIP) which supporting network-based mobility in hierarchical manner can provide optimal routing path in the tactical network environment, but centralized anchor is still remained a threat to the stability of the tactical network which changes its topology dynamically. In this paper, we propose PMIP-based distributed mobility management for the tactical network environment. From our design, routing paths are always configured in optimized way, as well as path is recovered quickly when the mobility anchor of user is failed. From numerical analysis, comparing to other mobility scheme, result shows that the proposed scheme can reduce packet transmission cost and latency in tactical network model.

• Journal of the Korean Society of Safety
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• v.39 no.1
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• pp.62-69
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• 2024

Ensuring swift on-site access to fire engines is crucial in preserving the golden time and minimizing damage. However, various mobility constraints in alleyways hinder the timely entry of fire engines to the fire scene, significantly impairing their initial response capabilities. Therefore, this study analyzed the significant mobility constraints of fire engines, focusing on Seoul, which has many old town areas. By leveraging survey responses from firefighting experts and actual observations, this study quantitatively assessed the frequency and severity of mobility constraint factors affecting the disaster responses of fire engines. Survey results revealed a consistent set of top five factors regarding the frequency and disturbance level, including illegally parked cars, narrow paths, motorcycles, poles, and awnings/banners. A comparison with actual road-view images showed notable consistency between the survey and observational results regarding the appearance frequency of mobility constraint factors in vulnerable areas in Seoul. Furthermore, the study emphasized the importance of tailored management strategies for each mobility constraint factor, considering its characteristics, such as dynamic or static. The findings of this study can serve as foundational data for creating more detailed fire safety maps and advancing technologies that monitor the mobility of fire engines through efficient vision-based inference using CCTVs in the future.

• Journal of Advanced Navigation Technology
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• v.27 no.4
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• pp.481-487
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• 2023

Recently, interest in UAM (Urban Air Mobility) has surged as a critical solution to urban traffic congestion and air pollution issues. However, efficient UAM operation requires accurate 3D Point Cloud data processing, particularly in separating the ground and objects. This paper proposes and validates a method for effectively separating ground and objects in a UAM environment, taking into account its dynamic and complex characteristics. Our approach combines attitude information from MEMS sensors with ground plane estimation using RANSAC, allowing for ground/object separation that isless affected by GPS errors. Simulation results demonstrate that this method effectively operates in UAM settings, marking a significant step toward enhancing safety and efficiency in urban air mobility. Future research will focus on improving the accuracy of this algorithm, evaluating its performance in various UAM scenarios, and proceeding with actual drone tests.

• The KIPS Transactions:PartC
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• v.12C no.5 s.101
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• pp.733-740
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• 2005

In this paper, we propose an approach to transport layer mobility support leveraging the SCTP extension dubbed dynamic address reconfiguration in IPv6 networks. Timing issues related to the end-to-end address management, and a novel error recovery mechanism associated with a handover are discussed. The proposed error recovery mechanism is analyzed and compared to that of the plain SCTP to show that it reduces the handover latency and error recovery time.

• Asia Marketing Journal
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• v.21 no.4
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• pp.1-24
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• 2020

The current research brings the spotlight onto customer Gapjil toward service employees. In an attempt to understand what brings Gapjil, the present article investigates the intertwined effects of perceived economic mobility (PEM), self-other referent priming (SORP), and temporal focus on Gapjil. Study 1 shows that PEM increases Gapjil among self-referent primed people, but not among other-referent primed people. Study 2 examining the role of temporal focus (present vs. future) reveals that the effect found in study 1 is replicated in the present focus, but the effect is reversed in the future focus. We explain this dynamic pattern of the 3-way interaction effect with a relative gratification and a motivation to attune the self to the perceived norm of the high class. We also discuss how to create a social atmosphere to lessen Gapjil in public and corporate communication strategies.

• ETRI Journal
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• v.45 no.5
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• pp.735-745
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• 2023

This paper surveys recent multiagent reinforcement learning and neural Myerson auction deep learning efforts to improve mobility control and resource management in autonomous ground and aerial vehicles. The multiagent reinforcement learning communication network (CommNet) was introduced to enable multiple agents to perform actions in a distributed manner to achieve shared goals by training all agents' states and actions in a single neural network. Additionally, the Myerson auction method guarantees trustworthiness among multiple agents to optimize rewards in highly dynamic systems. Our findings suggest that the integration of MARL CommNet and Myerson techniques is very much needed for improved efficiency and trustworthiness.