• Electronics and Telecommunications Trends
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• v.35 no.6
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• pp.137-149
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• 2020

Several multi-agent reinforcement learning (MARL) algorithms have achieved overwhelming results in recent years. They have demonstrated their potential in solving complex problems in the field of real-time strategy online games, robotics, and autonomous vehicles. However these algorithms face many challenges when dealing with massive problem spaces in sparse reward environments. Based on the centralized training and decentralized execution (CTDE) architecture, the MARL algorithms discussed in the literature aim to solve the current challenges by formulating novel concepts of inter-agent modeling, credit assignment, multiagent communication, and the exploration-exploitation dilemma. The fundamental objective of this paper is to deliver a comprehensive survey of existing MARL algorithms based on the problem statements rather than on the technologies. We also discuss several experimental frameworks to provide insight into the use of these algorithms and to motivate some promising directions for future research.

• Journal of the Economic Geographical Society of Korea
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• v.11 no.4
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• pp.647-660
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• 2008

The main thrusts of Canadian regional innovation policy lies in the two tract system. Federal government decides only the strategic research and development sectors and priorities, and then researchers and stockholders in the regions decide and implement the specific networking relationships and appropriate governance system. This paper reviewed the decentralized and market-friendly Canadian regional innovation support system and the characteristics of Canadian innovation clusters: Learning, Labour, Location, Leadership, Legislation/ Labs. finally, policy implications for Korean regional innovation system such as networking, formation of social capital, and business support systems are offered.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.34 no.4
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• pp.183-189
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• 2021

In recent years, active research has been devoted toward developing a monitoring system using ambient vibration data in order to quantitatively determine the deterioration occurring in a structure over a long period of time. This study developed a low-cost edge computing system that detects the abnormalities in structures by utilizing the dynamic characteristics acquired from the structure over the long term for ensemble learning. The system hardware consists of the Raspberry Pi, an accelerometer, an inclinometer, a GPS RTK module, and a LoRa communication module. The structural abnormality detection afforded by the ensemble learning using dynamic characteristics is verified using a laboratory-scale structure model vibration experiment. A real-time distributed processing algorithm with dynamic feature extraction based on the experiment is installed on the Raspberry Pi. Based on the stable operation of installed systems at the Community Service Center, Pohang-si, Korea, the validity of the developed system was verified on-site.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.260-263
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• 2023

Considering the growth of blockchain technology, the research aims to transform the efficiency of recommending optimal coffee suppliers within the complex supply chain network. This transformation relies on the extraction of vital transactional data and insights from stakeholders, facilitated by the dynamic interaction between the application interface (e.g., Rest API) and the blockchain network. These extracted data are then subjected to advanced data processing techniques and harnessed through machine learning methodologies to establish a robust recommendation system. This innovative approach seeks to empower users with informed decision-making abilities, thereby enhancing operational efficiency in identifying the most suitable coffee supplier for each customer. Furthermore, the research employs data visualization techniques to illustrate intricate clustering patterns generated by the K-Means algorithm, providing a visual dimension to the study's evaluation.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.777-779
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• 2024

현대 사회에서 인공지능은 다양한 분야에서 사용되며 발전하고 있다. 특히 의료, 공업, 경제, 농업, 정치 등에 영향을 미치며, 데이터 프라이버시 문제가 빈번히 발생한다. 이를 해결하기 위해 연합학습이 제안되었는데, 이는 로컬 디바이스에서 학습한 모델만을 중앙 서버로 전송하여 프라이버시를 보장하고 효율성을 높인다. 하지만 연합학습은 중앙 서버를 필요로 하므로 탈중앙적인 환경에서는 사용할 수 없는 단점이 있다. 이를 보완하기 위해 본 논문에서는 서버가 없는 다양한 환경에서 연합학습을 적용할 수 있는 비-완전 연결 분산형 연합학습 알고리즘을 소개한다. 비-완전 연결 분산형 연합학습 알고리즘은 모든 노드가 서로 연결 되어있는 상태가 아닌 특정 노드와만 연결 되어있는 형태로 대부분의 실전 분산형 환경에서 사용할 수 있다. 본 방식의 학습 정확도는 일반적인 머신러닝의 정확도와 비교하여 준수한 성능을 보여주고 있다.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.143-156
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• 2024

Federated Learning (FL) has emerged as a potent methodology for decentralized model training across multiple collaborators, eliminating the need for data sharing. Although FL is lauded for its capacity to preserve data privacy, it is not impervious to various types of privacy attacks. Differential Privacy (DP), recognized as the golden standard in privacy-preservation techniques, is widely employed to counteract these vulnerabilities. This paper makes a specific contribution by applying an existing, task-specific adaptive DP mechanism to the FL environment. Our comprehensive analysis evaluates the impact of this mechanism on the performance of a shared global model, with particular attention to varying data distribution and partitioning schemes. This study deepens the understanding of the complex interplay between privacy and utility in FL, providing a validated methodology for securing data without compromising performance.

• Journal of KSNVE
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• v.8 no.6
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• pp.1181-1192
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• 1998

This paper describes the performance of a full-authority neural network-based fault tolerant system within a flight control system. This fault tolerant flight control system integrates sensor and actuator failure detection, identification, and accommodation (SFDIA and AFDIA), The first task is achieved by incorporating a main neural network (MNN) and a set of n decentralized neural networks (DNNs) to create a system for achieving fault tolerant capabilities for a system with n sensors assumed to be without physical redundancy The second scheme implements the same main neural network integrated with three neural network controllers (NNCs). The function of NNCs is to regain equilibrium and to compensate for the pitching, rolling. and yawing moments induced by the failure. Particular emphasis is placed in this study toward achieving an efficient integration between SFDIA and AFDIA without degradation of performance in terms of false alarm rates and incorrect failure identification. The results of the simulation with different actuator and sensor failures are presented and discussed.

• Korean Journal of Health Education and Promotion
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• v.22 no.3
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• pp.97-107
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• 2005

This paper considers the possibilities of health promotion from the following perspectives; (1) IUHPE, (2) socio-cultural similarities, (3) action research, and (4) learning from our past. 1. The IUHPE values decentralized activities through regions, and countries such as Japan, Korea, Hong Kong, Taiwan and China belong to NPWP region. Since IUHPE World Conference was held in Japan in 1995, Japan used to occupy more than 60% of NPWP membership. After 2001, membership is increasing rapidly in Chinese speaking sub-region. The transnational collaboration is still in its beginning phase. 2. Confucianism is one of key points. Confucian tradition should not be seen only as obstacles but as advantages to seek a form of health promotion more acceptable in East Asia. 3 Within the new public health framework, people are expected to create and live their health. However, especially in Japan, the tendency of 'lacking of face-to-face explicit interactions' is still common at health-promotion settings as well as academic settings. Therefore, the author tried participatory approaches such as asking WIFY(interactive questions designed for subjects to review their daily life and environment) and as introducing round table interactions. So far, majority of participants welcome new trials. 4. The following social phenomena are comparatively discussed after Japanese invasion and occupation of Korea ended in 1945; status of oriental medicine, separation of dispensary services, and health promotion specialist as a national license. In contrast to Japanese' tendency of maintaining the status quo and postponing of substantial social change, trend toward rapid and dynamic social changes are more commonly observed in Korea. Although all of above possibilities are still in their beginning stages, they are going to offer interesting directions waiting for further challenges and accompanying researches.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.765-774
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• 2013

In this paper, we present a cooperative compressed spectrum sensing scheme for correlated signals in decentralized wideband cognitive radio networks. Compressed sensing is a signal processing technique that can recover signals which are sampled below the Nyquist rate with high probability, and can solve the necessity of high-speed analog-to-digital converter problem for wideband spectrum sensing. In compressed sensing, one of the main issues is to design recovery algorithms which accurately recover original signals from compressed signals. In this paper, in order to achieve high recovery performance, we consider the multiple measurement vector model which has a sequence of compressed signals, and propose a cooperative sparse Bayesian recovery algorithm which models the temporal correlation of the input signals.

• Journal of Information Processing Systems
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• v.11 no.3
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• pp.370-388
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• 2015

The localization of multi-agents, such as people, animals, or robots, is a requirement to accomplish several tasks. Especially in the case of multi-robotic applications, localization is the process for determining the positions of robots and targets in an unknown environment. Many sensors like GPS, lasers, and cameras are utilized in the localization process. However, these sensors produce a large amount of computational resources to process complex algorithms, because the process requires environmental mapping. Currently, combination multi-robots or swarm robots and sensor networks, as mobile sensor nodes have been widely available in indoor and outdoor environments. They allow for a type of efficient global localization that demands a relatively low amount of computational resources and for the independence of specific environmental features. However, the inherent instability in the wireless signal does not allow for it to be directly used for very accurate position estimations and making difficulty associated with conducting the localization processes of swarm robotics system. Furthermore, these swarm systems are usually highly decentralized, which makes it hard to synthesize and access global maps, it can be decrease its flexibility. In this paper, a simple pyramid RAM-based Neural Network architecture is proposed to improve the localization process of mobile sensor nodes in indoor environments. Our approach uses the capabilities of learning and generalization to reduce the effect of incorrect information and increases the accuracy of the agent's position. The results show that by using simple pyramid RAM-base Neural Network approach, produces low computational resources, a fast response for processing every changing in environmental situation and mobile sensor nodes have the ability to finish several tasks especially in localization processes in real time.