• 전력전자학회:학술대회논문집
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• 전력전자학회 2010년도 추계학술대회
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• pp.32-33
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• 2010

This paper describes a droop control method for the autonomous operation of DC distribution system using distributed generations and energy storage. The method suppress the circulating current, and each unit could be controlled autonomously without communication system. Detailed model of wind power generation, photo-voltaic generation, fuel-cell generation and battery was implemented with the user-defined model of PSCAD/EMTDC software that is coded with C-language. The simulation and experimental results confirms that the proposed DC distribution system make it feasible to provide power to the load stably and verify effectiveness of the proposed method.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제7권11호
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• pp.281-288
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• 2018

동일한 데이터를 여러 곳에 보관하는 분산원장을 특징으로 갖는 블록체인은 보안성과 안정성을 비롯한 여러 가지 기술적 특징을 가지며, 이로 인해 블록체인의 활용처에 관한 연구가 다양하게 이루어지고 있다. 본 논문에서는 공적장부의 하나인 부동산종합공부시스템에 블록체인을 적용하기 위해 고려할 사항들을 도출하고, 이를 바탕으로 블록체인 시스템 구성 방안과 합의 알고리즘을 포함하는 블록체인 참조 모델을 제시한다.

• Journal of information and communication convergence engineering
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• 제20권2호
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• pp.96-102
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• 2022

We aim to build predictive models for Airbnb's prices using a GPU-accelerated RAPIDS in a big data cluster. The Airbnb Listings datasets are used for the predictive analysis. Several machine-learning algorithms have been adopted to build models that predict the price of Airbnb listings. We compare the results of traditional and big data approaches to machine learning for price prediction and discuss the performance of the models. We built big data models using Databricks Spark Cluster, a distributed parallel computing system. Furthermore, we implemented models using multiple GPUs using RAPIDS in the spark cluster. The model was developed using the XGBoost algorithm, whereas other models were developed using traditional central processing unit (CPU)-based algorithms. This study compared all models in terms of accuracy metrics and computing time. We observed that the XGBoost model with RAPIDS using GPUs had the highest accuracy and computing time.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.327-347
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• 2024

Edge computing is frequently employed in the Internet of Vehicles, although the computation and communication capabilities of roadside units with edge servers are limited. As a result, to perform distributed machine learning on resource-limited MEC systems, resources have to be allocated sensibly. This paper presents an Improved MADDPG algorithm to overcome the current IoV concerns of high delay and limited offloading utility. Firstly, we employ the MADDPG algorithm for task offloading. Secondly, the edge server aggregates the updated model and modifies the aggregation model parameters to achieve optimal policy learning. Finally, the new approach is contrasted with current reinforcement learning techniques. The simulation results show that compared with MADDPG and MAA2C algorithms, our algorithm improves offloading utility by 2% and 9%, and reduces delay by 29.6%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권3호
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• pp.971-989
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• 2015

With the development of cloud computing, more and more Web services are deployed on geo-distributed datacenters and are offered to cloud users all over the world. Through service composition technologies, these independent fine-grain services can be integrated to value-added coarse-grain services. During the composition, a number of Web services may provide the same function but differ in performance. In addition, the distribution of cloud datacenters presents a geographically dispersive manner, which elevates the impact of the network on the QoS of composite services. So it is important to select an optimal composition path in terms of QoS when many functionally equivalent services are available. To achieve this objective, we first present a graph model that takes both QoS of Web services and QoS of network into consideration. Then, a novel approach aiming at selecting the optimal composition path that fulfills the user's end-to-end QoS requirements is provided. We evaluate our approach through simulation and compare our method with existing solutions. Results show that our approach significantly outperforms existing solutions in terms of optimality and scalability.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권2호
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• pp.55-60
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• 2002

This paper presents a controller for the multiple DC motors using the CAN(Controller Area Network). The controller has a benefit of reducing the cable connections and making the controller boards compact through the network including expansibility. CAN, among the field buses, is a serial communication methodology which has the physical layer and the data link layer in the ISO's OSI (Open System Interconnect) 7 layered reference model. It provides the user with many powerful features including multi-master functionality and the ability to broadcast / multicast telegrams. When we use a microprocessor chip embedding the CAN function, the system becomes more economical and reliable to react shortly in the data transmission. The controller, we proposed, is composed of two main controllers and a sub controller, which have built with a one-chip microprocessor having CAN function. The sub controller is plugged into the Pentium PC to perform a CAN communication, and connected to the main controllers via the CAN. Main controllers are responsible for controlling two motors respectively. Totally four motors, actuators for the biped robot in our laboratory, are controlled in the experiment. We show that the four motors are controlled properly to actuate the biped robot through the network in real time.

• 한국통신학회논문지
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• 제14권3호
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• pp.197-206
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• 1989

본 논문에서는 통신 프로토콜의 성능 분석을 위한 개선된 알고리즘을 제안한다. 이는 분산 시스템의 성능을 모델링하고 평가하는데 성공적으로 사용되어져 온 시간을 포함하는 확장된 Timed Petri Net 모델을 분석하는 알고리즘이다. 제안된 알고리즘은 일반적인 Timed Petir Net가 free-chioce 그리고 safe net 이라는 모델링의 제약을 완화하여, 제한적인 병행처리의 모델링을 허용한다. 그리고 Timed Reachability 그래프의 상태공간을 감소시키기 위해, 최다 수행규칙고 동시점화 트랜지션 집합을 이용한다. 알고리즘의 유용성 및 효율성을 보이기 위하여 Timed Reachability 그래프의 구성과 분석과정을 제시하고, 통신 프로토콜에 적용시켰다.

• 유통과학연구
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• 제21권12호
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• pp.35-45
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• 2023

Purpose: This study analyzes civil servants' trust in cash waqf institution and their relationship with commitment. We use Key Mediating Variables (KMV) Theory with variables such as board benevolence, board integrity, board ability, board opportunism, communication, and accountability to predict the trust and commitment of civil servants in providing cash waqf. Research design, data and methodology: We used a quantitative research approach and an online questionnaire distributed to civil servants in Gorontalo Province; 410 respondents were obtained. Structural Equation Modeling (SEM) was used, utilizing the SEM-PLS software to analyze the data. Result: This study found that the variables board benevolence, board ability, board integrity, board opportunism, communication, and accountability affected trust. As for the variable of accountability, the hypothesis was rejected Conclusion: The communication variable of cash waqf institutions has a strong influence in increasing the confidence of civil servants in providing cash endowments. However, trust in waqf institutions may diminish due to low accountability and poor governance of cash waqf institutions. The trust of civil servants in cash waqf institutions in the future will increase their commitment to waqf.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권6호
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• pp.1462-1477
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• 2024

With the evolving complexity of connected vehicle features, the volume and diversity of data generated during driving continue to escalate. Enabling data sharing among interconnected vehicles holds promise for improving users' driving experiences and alleviating traffic congestion. Yet, the unintentional disclosure of users' private information through data sharing poses a risk, potentially compromising the interests of vehicle users and, in certain cases, endangering driving safety. Federated learning (FL) is a newly emerged distributed machine learning paradigm, which is expected to play a prominent role for privacy-preserving learning in autonomous vehicles. While FL holds significant potential to enhance the architecture of the Internet of Vehicles (IoV), the dynamic mobility of vehicles poses a considerable challenge to integrating FL with vehicular networks. In this paper, a novel clustered FL framework is proposed which is efficient for reducing communication and protecting data privacy. By assessing the similarity among feature vectors, vehicles are categorized into distinct clusters. An optimal vehicle is elected as the cluster head, which enhances the efficiency of personalized data processing and model training while reducing communication overhead. Simultaneously, the Local Differential Privacy (LDP) mechanism is incorporated during local training to safeguard vehicle privacy. The simulation results obtained from the 20newsgroups dataset and the MNIST dataset validate the effectiveness of the proposed scheme, indicating that the proposed scheme can ensure data privacy effectively while reducing communication overhead.

• 한국융합학회논문지
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• 제9권6호
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• pp.1-7
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• 2018

최근 IT 기술이 발달함에 따라 병원에서 사용되고 있는 의료 장비도 고사양의 성능을 요구하고 있다. 그러나, 사용자는 사용자의 상황에 따라 서로 다른 병원을 내원하기 때문에, 병원에서 진료 받은 의료 정보가 병원마다 분산되어 있다. 본 논문에서는 서로 다른 병원에 내원한 사용자의 헬스케어 정보 수집을 위해서 병원에 저장되어 있는 사용자의 헬스케어 정보를 효율적으로 통합하기 위한 모델을 제안한다. 제안모델은 사용자 중심의 헬스케어 정보 수집을 위해서 개인 웨어러블 장치로부터 수집된 사용자의 헬스케어 정보를 서로 동기화한다. 또한, 제안 모델은 헬스케어 서비스 센터와 데이터 공유를 원활하게 수행하기 위해서 클라우드 환경에 존재하는 데이터베이스에서 사용자의 헬스케어 정보와 관련된 무결성 및 유효성 검사를 수행한다. 특히, 제안모델은 모바일 플랫폼으로부터 수집된 사용자의 헬스케어 정보를 원활하게 관리하기 위해서 트리기반의 데이터 처리를 수행할 수 있도록 하였다.