• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.101-104
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• 2007

Efficient Query processing and optimization are critical for reducing network traffic and decreasing latency of query when accessing and manipulating sensor data of large-scale sensor networks. Currently it has been studied in sensor database projects. These works have mainly focused on in-network query processing for sensor networks and assumes homogeneous sensor networks, where each sensor network has same hardware and software configuration. In this paper, we present a framework for efficient query processing over heterogeneous sensor networks. Our proposed framework introduces query processing paradigm considering two heterogeneous characteristics of sensor networks: (1) data dissemination approach such as push, pull, and hybrid; (2) query processing capability of sensor networks if they may support in-network aggregation, spatial, periodic and conditional operators. Additionally, we propose multi-query optimization strategies supporting cross-translation between data acquisition query and data stream query to minimize total cost of multiple queries. It has been implemented in WSN middleware, COSMOS, developed by ETRI.

• Nuclear Engineering and Technology
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• v.56 no.3
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• pp.993-1001
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• 2024

The TANDEM project is a European initiative funded under the EURATOM program. The project started on September 2022 and has a duration of 36 months. TANDEM stands for Small Modular ReacTor for a European sAfe aNd Decarbonized Energy Mix. Small Modular Reactors (SMRs) can be hybridized with other energy sources, storage systems and energy conversion applications to provide electricity, heat and hydrogen. Hybrid energy systems have the potential to strongly contribute to the energy decarbonization targeting carbon-neutrality in Europe by 2050. However, the integration of nuclear reactors, particularly SMRs, in hybrid energy systems, is a new R&D topic to be investigated. In this context, the TANDEM project aims to develop assessments and tools to facilitate the safe and efficient integration of SMRs into low-carbon hybrid energy systems. An open-source "TANDEM" model library of hybrid system components will be developed in Modelica language which, by coupling, will extend the capabilities of existing tools implemented in the project. The project proposes to specifically address the safety issues of SMRs related to their integration into hybrid energy systems, involving specific interactions between SMRs and the rest of the hybrid systems; new initiating events may have to be considered in the safety approach. TANDEM will study two hybrid systems covering the main trends of the European energy policy and market evolution at 2035's horizon: a district heating network and power supply in a large urban area, and an energy hub serving energy conversion systems, including hydrogen production; the energy hub is inspired from a harbor-like infrastructure. TANDEM will provide assessments on SMR safety, hybrid system operationality and techno-economics. Societal considerations will also be encased by analyzing European citizen engagement in SMR technology safety.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.1
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• pp.68-82
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• 2011

Wireless mesh networks (WMNs) provide high-speed backbone networks without any wired cable. Many researchers have tried to increase network throughput by using multi-channel and multi-radio interfaces. A multi-radio multi-channel WMN requires channel assignment algorithm to decide the number of channels needed for each link. Since the channel assignment affects routing and interference directly, it is a critical component for enhancing network performance. However, the optimal channel assignment is known as a NP complete problem. For high performance, most of previous works assign channels in a centralized manner but they are limited in being applied for dynamic network environments. In this paper, we propose a simple flow estimation algorithm and a hybrid channel assignment algorithm. Our flow estimation algorithm obtains aggregated flow rate information between routers by packet sampling, thereby achieving high scalability. Our hybrid channel assignment algorithm initially assigns channels in a centralized manner first, and runs in a distributed manner to adjust channel assignment when notable traffic changes are detected. This approach provides high scalability and high performance compared with existing algorithms, and they are confirmed through extensive performance evaluations.

• IMMUNE NETWORK
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• v.2 no.3
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• pp.137-141
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• 2002

Among the members of the inhibitor of apoptosis (IAP) protein family, only Livin and survivin have been reported to have variant forms. We have found a variant form of c-IAP2 through the interaction with the X protein of HBV using the yeast two-hybrid system. In contrast to the wild-type c-IAP2, the variant form has two stretches of sequence in the RING domain that are repeated in the C-terminus that would disrupt the RING domain. We demonstrate that the variant form has an inhibitory effect on TNF-mediated $NF-{\kappa}B$ activation unlike the wild-type c-IAP2, which increases TNFmediated $NF-{\kappa}B$ activation. These results suggest that this variant form has different activities from the wild-type and the RING domain may be involved in the regulation of TNF-induced $NF-{\kappa}B$ activation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.12B
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• pp.1450-1458
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• 2011

The HPcN (Hybrid & high Performance Convergence Network) in research networks means environment which can provide both computing resource such as supercomputer, cluster and network resource to application researchers in the field of medical, bio, aerospace and e-science. The most representative research network in Korea, KREONET has been developing following technologies through the HERO (Hybrid Networking project for research oriented infrastructure) from 200S. First, we have constructed and deployed a control plane technology which can provide a connection oriented network dynamically. Second, the integrated resource management system technology has been developing for reservation and allocation of both computing and network resources, whenever users want to utilize them. In this paper, a testbed network is presented, which is possible to reserve and allocate network resource using the integrated resource management system. We reserve network resource through GNSI (Grid Network Service Interface) messages between GRS (Global Resource Scheduler) and NRM (Network Resource Manager) and allocate network resource through GUNI (Grid User Network Interface) messages between the NRM (network resource manager) and routers, based on reservation information provided from a user on the web portal. It is confirmed that GUNI interface messages are delivered from the NRM to each router at the starting of reservation time and traffic is transmitted through LSP allocated by the NRM.

• ETRI Journal
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• v.29 no.2
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• pp.162-168
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• 2007

A new scheme for bi-directional HDTV/Gigabit Ethernet/CATV transmission over a hybrid dense-wavelength-division-multiplexing passive optical network (DWDM-PON) is proposed and demonstrated. It is based on injection-locked vertical-cavity surface-emitting lasers and distributed-feedback laser diodes as transmitters. Services with 129 HDTV channels, a 1.25 Gbps Gigabit Ethernet connection, and 77 CATV channels are successfully demonstrated over 40 km single-mode fiber links. Good performance of bit error rate, carrier-to-noise ratio, composite second order, and composite triple beat is achieved in our proposed bidirectional DWDM-PON.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.708-711
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• 1997

This paper proposes the hybrid algorithm for the optimization of the structure and parameters of the fuzzy neural networks by genetic algorithms (GA) to improve the behaviour and the design of fuzzy neural networks. Fuzzy neural networks have a distinguishing feature in that they can possess the advantage of both neural networks and fuzzy systems. In this way, we can bring the low-level learning and computational power of neural networks into fuzzy systems and also high-level, human like IF-THEN rule thinking and reasoning of fuzzy systems into neural networks. As a result, there are many research works concerning the optimization of the structure and parameters of fuzzy neural networks. In this paper, we propose the hybrid algorithm that can optimize both the structure and parameters of fuzzy neural networks. Numerical example is provided to show the advantages of the proposed method.

• Electronics and Telecommunications Trends
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• v.11 no.4 s.42
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• pp.63-75
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• 1996

본 고에서는 일반 가정 및 소규모 업무용 가입자들에 대한 광대역화의 가장 경제적인 대안으로 관심을 끌고 있는 Hybrid fiber-coax 구조의 CATV 전송망에 대한 표준화 동향에 대해 조사 분석한 내용을 기술한다. HFC 액세스망에 대한 표준화는 IEEE 802.14 그룹, DAVIC, ATM Forum, IETF(Internet Engineering Task Force)와 Cable TV업계 등 여러기구에서 추진하고 있다. 지금까지 물리계층, MAC계층 등에 대한 주요 표준규격에 대해 DAVIC과 IEEE 802.14에서 주도하고 있으나, 앞으로는 데이터 서비스에 상대적 우선 순위를 두고 표준화를 추진하는 IEEE 802.14그룹에서 주도하게 될 것으로 보인다. 또한 인터넷 접속과 고속 데이터 서비스를 위한 인터페이스와 IP 접속 프로토콜에 대한 표준화를 발 빠르게 추진하고 있는 IETF와 MCNS(Multimedia Cable Network System) 등의 역할이 커질 것으로 전망된다.

• ETRI Journal
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• v.45 no.3
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• pp.433-447
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• 2023

Critical issues such as connection congestion, long transmission delay, and packet loss become even worse during epidemic, disaster, and so on. In this study, a link load balancing method is proposed to address these issues on the data plane, a plane of the software-defined network (SDN) architecture. These problems are NP-complete, so a meta-heuristic approach, discrete particle swarm optimization, is used with a novel hybrid cost function. The superiority of the proposed method over existing methods in the literature is that it provides link and switch load balancing simultaneously. The goal is to choose a path that minimizes the connection load between the source and destination in multipath SDNs. Furthermore, the proposed work is dynamic, so selected paths are regularly updated. Simulation results prove that with the proposed method, streams reach the target with minimum time, no loss, low power consumption, and low memory usage.

• Earthquakes and Structures
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• v.25 no.1
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• pp.15-26
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• 2023

It is of great importance to assess the residual displacement demand in the performance-based seismic design. In this paper, a hybrid deep learning model for predicting the residual displacement spectra under near-fault (NF) ground motions is proposed by combining the long short-term memory network (LSTM) and back-propagation (BP) network. The model is featured by its capacity of predicting the residual displacement spectrum under a given NF ground motion while considering the effects of structural parameters. To construct this model, 315 natural and artificial NF ground motions were employed to compute the residual displacement spectra through elastoplastic time history analysis considering different structural parameters. Based on the resulted dataset with a total of 9,450 samples, the proposed model was finally trained and tested. The results show that the proposed model has a satisfactory accuracy as well as a high efficiency in predicting residual displacement spectra under given NF ground motions while considering the impacts of structural parameters.