• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.853-856
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• 2008

With development of computer network technique it joins in recently and ubiquitous computing comes true the research for is actively advanced. The creation rule of registration and search of the event is necessary because the USN sensor data must be possible to specific the multi condition of event which relates on using time. And for using a numerous sensor data provision service, service provider can publish its service in standardized registry for users to search and use the service registry. Currently there is the web service for a WS-Eventing and a UDDI with standard, but the event and the service registry is unnecessary or there is a portion which is insufficient. In this paper, we studied standardization of event definition for USN application service and service registry structure, standard API.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39B no.10
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• pp.645-653
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• 2014

Recently, the ocean field researches such as offshore plant, ocean survey and underwater monitoring systems are garnering the attention from both academy and industry. However, the communication in underwater environment is very difficult because of the unique irregular features in water. This is the reason that the application of terrestrial protocols to the water environment is not proper. This paper proposes a routing algorithm that can enhance communication reliability by utilizing channel properties in underwater environment. We address two problems that lead to the poor communication performance, signal attenuation and multi-path problem in water. Overcoming these problems, the proposed algorithm ensures high packet delivery ratio and low transmission delay. Also, this paper evaluates the performance through simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.5
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• pp.886-891
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• 2015

Recently, The World Health Organization announced that harms human health because of air pollution that are emerging as threats to human health worldwide. according to the, Seoul, 2011 According to a July 2014 - Public Facilities indoor air quality measurements, were examined to be in violation of indoor air pollution standards in most multi-use facility. Indoor air pollution resulting from this is present in the paper, and cause disease, such as pulmonary disease, asthma, bronchitis and to In this connection, the measurement of indoor air quality by using the environment sensor, analyzing the measured data to generate an actuator signal required for ventilation and improve indoor air quality by implementing a monitoring system with real-time measurement, autonomously managing the air quality in our lives so that it can be.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.2
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• pp.367-372
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• 2017

LED illumination has been developed from simple illumination to human oriented multi-functional system depending on the IT convergence trends. Thus, the convergence of IT technology such as various types of sensor and communication functions with application software can make saving of energy, and there are also several needs about the tailor-made illuminations requirements for the diverse users who want to take their own colors, strengths of the light. For the adaption of these kinds of intelligent system illumination, user oriented sensor and control should be possible. The LED lighting control system transmits and receives data via the Bluetooth mesh network and provides functions such as LED lighting control, LED lighting condition monitoring, energy usage monitoring, and energy peak point management. Therefore, in this paper, we suggest wireless communication platform and control system for the LED illumination control. We can expect life extension of LED illumination, and energy saving through this suggested system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.4
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• pp.1317-1341
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• 2021

Nowadays, the Internet of Things (IoT) is adopted to enable effective and smooth communication among different networks. In some specific application, the Wireless Sensor Networks (WSN) are used in IoT to gather peculiar data without the interaction of human. The WSNs are self-organizing in nature, so it mostly prefer multi-hop data forwarding. Thus to achieve better communication, a cross-layer routing strategy is preferred. In the cross-layer routing strategy, the routing processed through three layers such as transport, data link, and physical layer. Even though effective communication achieved via a cross-layer routing strategy, energy is another constraint in WSN assisted IoT. Cluster-based communication is one of the most used strategies for effectively preserving energy in WSN routing. This paper proposes a Bio-inspired cross-layer routing (BiHCLR) protocol to achieve effective and energy preserving routing in WSN assisted IoT. Initially, the deployed sensor nodes are arranged in the form of a grid as per the grid-based routing strategy. Then to enable energy preservation in BiHCLR, the fuzzy logic approach is executed to select the Cluster Head (CH) for every cell of the grid. Then a hybrid bio-inspired algorithm is used to select the routing path. The hybrid algorithm combines moth search and Salp Swarm optimization techniques. The performance of the proposed BiHCLR is evaluated based on the Quality of Service (QoS) analysis in terms of Packet loss, error bit rate, transmission delay, lifetime of network, buffer occupancy and throughput. Then these performances are validated based on comparison with conventional routing strategies like Fuzzy-rule-based Energy Efficient Clustering and Immune-Inspired Routing (FEEC-IIR), Neuro-Fuzzy- Emperor Penguin Optimization (NF-EPO), Fuzzy Reinforcement Learning-based Data Gathering (FRLDG) and Hierarchical Energy Efficient Data gathering (HEED). Ultimately the performance of the proposed BiHCLR outperforms all other conventional techniques.

• Proceedings of the Korean Information Science Society Conference
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• 2007.10d
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• pp.176-179
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• 2007

센서 네트워크는 제한된 에너지를 가지는 작은 노드들로 구성이 된다. 제한된 자원을 가지는 센서 네트워크에서 저 전력에 대한 연구는 핵심 분야로써, 다양한 연구가 진행 중이다. 센서 네트워크에서 에너지 효율을 높이는 방법들이 많이 연구되고 있다. 센서 네트워크에서 가장 큰 에너지 손실을 가져오는 부분은 통신 부분이며, 질의 기반 센서 네트워크에서는 질의를 배포하게 될 때 에너지 소비의 주요 요소들이 발생하게 된다. 따라서, 본 논문은 베이스스테이션으로 다중 질의를 요청할 때 질의 합성을 통해 전달되는 질의 수를 최소화하여 질의의 배포에 따른 에너지 소비 요소를 줄이는데 그 목적이 있다. 질의는 조건절에서 특정 속성에 대해서 판별하는데, 본 논문에서 제안하는 방법은 속성에 따라 질의를 합성하여 원본 질의의 개수보다 적게 하는 것이다. 제안 알고리즘을 통해 합성된 질의가 센서 노드에게 전해지는 과정에서 데이터의 송수신 횟수 및 overhead를 줄일 수 있으며, 또한 센서 노드는 합성된 질의로 센싱 횟수를 줄 일 수 있어 에너지 효율을 높일 수 있다. 본 논문에서 제안한 방법과 일반적인 방법을 비교하기 위해 에너지 소모 절감 예를 살펴봤다.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.11
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• pp.2875-2882
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• 2000

Typically, the ultrasonic sensors can be used in navigation systems for modeling of the enviornment, obstacle avoidance, and map building. In this paper, we tried to approach an object classification method using the range data of the ultrasonic sensors. A characterization of the sonar scan is described that allows the differentiation of planes, corners, edges, cylindrical and rectangular pillars by processing the scanned data from three sonars. To use the data from the ultrasonic sensors as input to the neural networks, we have introduced a clustering, threshold, and bit operation algorithm for the obtained raw data, After repeated training of the neural network, the performance of the proposed method was obtained through experiments. Also, the recognition ranges of the proposed method were investigated. As a result of experiments, we found that the proposed method successfully recognized the objects within the accuracy of 78%.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.11
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• pp.872-879
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• 2017

This work presents a cable fault monitoring system based on the differential frequency domain reflectometry using a compact impedance analyser which is composed of a direct digital synthesizer, an op amp and a gain/phase detector with a micro controller. The proposed system can replace expensive vector network analysers for frequency domain reflectometry and therefore be deployed in sensor networks for long term multi-point cable monitoring. Effectiveness of the system is experimentally confirmed by diagnosing the status of the power cable.

• Journal of Korean Society of Transportation
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• v.19 no.6
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• pp.171-182
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• 2001

Travel Time is an important characteristic of traffic conditions in a road network. Currently, there are so many road users to get a unsatisfactory traffic information that is provided by existing collection systems such as, Detector, Probe car, CCTV and Anecdotal Report. This paper presents the results achieved with Data Fusion Model, Hybrid Neuro Fuzzy System for on - line estimation of travel times using RTMS(Remote Traffic Microwave Sensor) and Probe Data in the signalized arterial road. Data Fusion is the most important process to compose the various of data which can present real value for traffic situation and is also the one of the major process part in the TIC(Traffic Information Center) for analyzing and processing data. On-line travel time estimation methods(FALEM) on the basis of detector data has been evaluated by real value under KangNam Test Area.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.51.2-51.2
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• 2019

Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this study, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to solar images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of SDO/EUV images from SDO/HMI magnetograms, (2) Generation of backside magnetograms from STEREO/EUVI images, (3) Generation of EUV & X-ray images from Carrington sunspot drawing, and (4) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.