• Journal of Nuclear Fuel Cycle and Waste Technology(JNFCWT)
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• v.8 no.4
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• pp.293-301
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• 2010

Optical fiber cable, as a sensor, was installed on the wall of KAERI(Korea Atomic Energy Research Institute) Underground Research Tunnel(KURT) in order to monitor the physical stability of the tunnel, which was constructed for technical development and demonstration of radioactive waste disposal. This monitoring system has two simultaneous measurements of temperature and strain over time using Brillouin backscatter. According to the results of the monitoring from Jan. 2008 to Nov. 2009, there is no significant displacement or movement at the tunnel wall However, the cumulative volume of total strain increased slightly as time passes with the comparison of the reference observation, which was measured in Jan. 2008. The change in cumulative volume of total strain indicates that the strain level had been affected by saturation and de-saturation phenomena due to groundwater fluctuation at several points at KURT. This system is based on the distributed sensing technique concept, not point sensing. By using this system, a displacement can be detected with the range from $20{\mu}{\varepsilon}$ to $28,000{\mu}{\varepsilon}$ every 1m interval in minimum. A temperature variation can be monitored at every 0.5m interval with the resolution of 0.01 in minimum. Based on the study, this monitoring system is potentially applicable to long term monitoring systems for radioactive waste disposal project as well as other structures and underground openings.

• Journal of Korea Multimedia Society
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• v.12 no.7
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• pp.962-970
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• 2009

This paper presents a data gathering and scheduling scheme for wireless sensor networks. We use a data gathering tree for sending the data from the sensor node to the base station. For an energy efficient operation of the sensor networks in a distributed manner, a time tree is built in order to reduce the collision probability and to minimize the total energy required to send data to the base station. A time tree is a data gathering tree where the base station is the root and each sensor node is either a relaying or a leaf node of the tree. Each tree operates in a different time schedule with possible different activation rate. Through the simulation, we found that the proposed scheme that uses time trees shows better characteristics in energy and data arrival rate when compared with other schemes such as SMAC and DMAC.

• Journal of Digital Convergence
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• v.10 no.1
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• pp.371-378
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• 2012

Through sensor nodes it can obtain behavior, condition, location of objects. Generally speaking, sensor nodes are very limited because they have a battery power supply. Therefore, for collecting sensor data, efficient energy management is necessary in order to prolong the entire network survival. In this paper, we propose a method that increases energy efficiency to be self-configuring by distributed sensor nodes per cluster. The proposed method is based on the ANTCLUST. After measuring the similarity between two objects it is method that determine own cluster. It applies a colonial closure model of ant. The result of an experiment, it showed that the number of alive nodes increased 27% than existing clustering methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.4
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• pp.665-683
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• 2011

Wireless sensor networks (WSN) consist of a large amount of sensor nodes distributed in a certain region. Due to the limited battery power of a sensor node, lots of energy-efficient schemes have been studied. Clustering is primarily used for energy efficiency purpose. However, clustering in WSNs faces several unattained issues, such as ensuring connectivity and scheduling inter-cluster transmissions. In this paper, we propose a location-based spiral clustering (LBSC) algorithm for improving connectivity and avoiding inter-cluster collisions. It also provides reliable location aware routing paths from all cluster heads to a sink node during cluster formation. Proposed algorithm can simultaneously make clusters in four spiral directions from the center of sensor field by using the location information and residual energy level of neighbor sensor nodes. Three logical addresses are used for categorizing the clusters into four global groups and scheduling the intra- and inter-cluster transmission time for each cluster. We evaluated the performance with simulations and compared it with other algorithms.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1325-1328
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• 2007

When we visualize the sound field radiated from a spherical sound source, spherical acoustic holography is proper among acoustic holography methods. However, there are measurement errors due to sensor position mismatch, sensor mismatch, directivity of sensor, and background noise. These errors are amplified if one predicts the pressures close to the sources: backward prediction. The goal of this paper is to quantitatively examine the effects of the error due to sensor position mismatch on acoustic pressure estimation. This paper deals with the cases of which the measurement deviations are distributed irregularly on the hologram plane. In such cases, one can assume that the measurement is a sample of many measurement events, and the cause of the measurement error is white noise on the hologram plane. Then the bias and random error are derived mathematically. In the results, it is found that the random error is important in the backward prediction. The relationship between the random error amplification ratio and the measurement parameters is derived quantitatively in terms of their energies.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.7
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• pp.2488-2511
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• 2015

Data redundancy has high impact on Wireless Sensor Network's (WSN) performance and reliability. Spatial and temporal similarity is an inherent property of sensory data. By reducing this spatio-temporal data redundancy, substantial amount of nodal energy and bandwidth can be conserved. Most of the data gathering approaches use either temporal correlation or spatial correlation to minimize data redundancy. In Collective Prediction exploiting Spatio Temporal correlation (CoPeST), we exploit both the spatial and temporal correlation between sensory data. In the proposed work, the spatial redundancy of sensor data is reduced by similarity based sub clustering, where closely correlated sensor nodes are represented by a single representative node. The temporal redundancy is reduced by model based prediction approach, where only a subset of sensor data is transmitted and the rest is predicted. The proposed work reduces substantial amount of energy expensive communication, while maintaining the data within user define error threshold. Being a distributed approach, the proposed work is highly scalable. The work achieves up to 65% data reduction in a periodical data gathering system with an error tolerance of 0.6℃ on collected data.

• Journal of the Korea Institute of Military Science and Technology
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• v.11 no.5
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• pp.58-70
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• 2008

Earlier researches on Sensor Networks preferred symmetric key-based authentication schemes in consideration of limitations in network resources. However, recent advancements in cryptographic algorithms and sensor-node manufacturing techniques have opened suggestion to public key-based solutions such as Merkle tree-based schemes. These previous schemes, however, must perform the authentication process one-by-one in hierarchical manner and thus are not fit to be used as primary authentication methods in sensor networks which require mass of multiple authentications at any given time. This paper proposes a new concept of public key-based authentication that can be effectively applied to sensor networks. This scheme is based on exponential distributed data concept, a derivative from Shamir's (t, n) threshold scheme, in which the authentication of neighbouring nodes are done simultaneously while minimising resources of sensor nodes and providing network scalability. The performance advantages of this scheme on memory usage, communication overload and scalability compared to Merkle tree-based authentication are clearly demonstrated using performance analysis.

• Journal of the Korean Society of Visualization
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• v.9 no.2
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• pp.34-39
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• 2011

This paper presents a novel direct fabrication method of the thin metal film RTD temperature sensor array on an arbitrary curved surface by using MEMS technology to measure a distributed temperature field up to $300^{\circ}C$ without disturbing a fluid flow. In order to overcome the difficulty in the three dimensional photography of sensor patterning, the UV pre-irradiated photosensitive dry film resist technology has been developed newly. This method was applied to the fabrication of the temperature sensor array on a glass tube, which is arranged parallel and transverse to a main flow. Gold was used as a temperature sensing material. The resistance change was measured in a thermally controlled oven by increasing the environmental temperature. The linear increase in resistance change and a constant slope were obtained. Also, the sensitivity of each RTD temperature sensor was evaluated.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.54 no.3
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• pp.156-165
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• 2005

Human being recognizes the physical world by integrating a great variety of sensory inputs, the information acquired by their own action, and their knowledge of the world using hierarchically parallel-distributed mechanism. In this paper, authors propose the sensor fusion system that can recognize multiple 3D objects from 2D projection images and tactile informations. The proposed system focuses on improving recognition performance of 3D objects. Unlike the conventional object recognition system that uses image sensor alone, the proposed method uses tactual sensors in addition to visual sensor. Neural network is used to fuse the two sensory signals. Tactual signals are obtained from the reaction force of the pressure sensors at the fingertips when unknown objects are grasped by four-fingered robot hand. The experiment evaluates the recognition rate and the number of learning iterations of various objects. The merits of the proposed systems are not only the high performance of the learning ability but also the reliability of the system with tactual information for recognizing various objects even though the visual sensory signals get defects. The experimental results show that the proposed system can improve recognition rate and reduce teeming time. These results verify the effectiveness of the proposed sensor fusion system as recognition scheme for 3D objects.

• Spatial Information Research
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• v.19 no.1
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• pp.127-133
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• 2011

Recently, with the advances in sensor techniques and net work computing, Ubiquitous Sensor Network (USN) has been received a lot of attentions from various communities. The sensor nodes distributed in the sensor network tend to continuously generate a large amount of data, which is called stream data. Sensor stream data arrives in an online manner so that it is characterized as high-speed, real-time and unbounded and it requires fast data processing to get the up-to-date results. The data stream has many application domains such as traffic analysis, physical distribution, U-healthcare and so on. Therefore, there is an overwhelming need of a USN middleware for processing such online stream data to provide corresponding services to diverse applications. In this paper, we propose a novel USN middleware which can provide users both context-aware service and meaningful sequential patterns. Our proposed USN middleware is mainly focused on location based applications which use stream location data. We also show the implementation of our proposed USN middleware. By using the proposed USN middleware, we can save the developing cost of providing context aware services and stream sequential patterns mainly in location based applications.