• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.94.4-94
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• 2002

Traditional control systems that consist of sensors, actuators and a controller centralized and connected with point-to-point links, have become distributed because of their performance limits and maintenance problems. Sensors and actuators are changed to smart devices having a processor and these devices and controllers are connected with fieldbuses such as Profibus, FIP, CAN, LonWorks and so on. Because they are distributed, it takes any delay to transmit data from sensor to controller and data from controller to actuator according to network characteristic. Also, the execution times of tasks in a node are not regular and depend on the node characteristic and the number of tasks and so on...

• Journal of Sensor Science and Technology
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• v.22 no.4
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• pp.297-301
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• 2013

We proposed and demonstrated a distributed fiber-optic overheating detection sensor using optical time domain refrectometry. With increased of temperature the optical fiber is bended by a bi-metal and it result in optical leaky loss of the fiber. The sensor structure is designed in such a way that the signal of overheating is happen when the temperature exceeding a threshold temperature and the optical fiber is protected from excess bending.

• Journal of information and communication convergence engineering
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• v.9 no.2
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• pp.155-160
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• 2011

In distributed source localization where sensors transmit measurements to a fusion node, we address the sensor selection problem where the goal is to find the best set of sensors that maximizes localization accuracy when quantization of sensor measurements is taken into account. Since sensor selection depends heavily upon rate assigned to each sensor, joint optimization of rate allocation and sensor selection is required to achieve the best solution. We show that this task could be accomplished by solving the problem of allocating rates to each sensor so as to minimize the error in estimating the position of a source. Then we solve this rate allocation problem by using the generalized BFOS algorithm. Our experiments demonstrate that the best set of sensors obtained from the proposed sensor selection algorithm leads to significant improvements in localization performance with respect to the set of sensors determined from a sensor selection process based on unquantized measurements.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2531-2533
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• 2002

In this paper, we evaluate feasibility of an object-oriented method in a distributed real-time control environment through the prediction of delay expected. We adopt CAN as the distributed network and the application layer of the CAN is composed of client/server communication model of COM and surroundings for the support of real-time capability of the COM. Mathematical models formalizing delays which are predicted to invoke in the COM architecture are proposed. Sensors and actuators which are widely used in distributed network-based systems are represented by COM objects in this paper. It is expected that the mathematical models can be used to protect distributed network-based systems from violation of real-time features by the COM.

• Structural Engineering and Mechanics
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• v.11 no.6
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• pp.617-636
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• 2001

For the potential application of smart materials to seismic structural control, this paper reviews seismic control techniques for civil engineering structures, and developments of smart materials for vibration and noise control. Analytical and finite element methods adopted for the design of distributed sensors/actuators using piezoelectric materials are discussed. Investigation of optimum position of sensors/actuators and damping are also outlined.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.101-110
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• 2004

The robots that will be needed in the near future are human-friendly robots that are able to coexist with humans and support humans effectively. To realize this, humans and robots need to be in close proximity to each other as much as possible. Moreover, it is necessary for their interactions to occur naturally. It is desirable for a robot to carry out human following, as one of the human-affinitive movements. The human-following robot requires several techniques: the recognition of the moving objects, the feature extraction and visual tracking, and the trajectory generation for following a human stably. In this research, a predictable intelligent space is used in order to achieve these goals. An intelligent space is a 3-D environment in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to follow a walking human using distributed intelligent sensors as stably and precisely as possible. The moving objects is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to follow the walking human, the linear and angular velocities are estimated and utilized. The computer simulation and experimental results of estimating and following of the walking human with the mobile robot are presented.

• Smart Structures and Systems
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• v.6 no.5_6
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• pp.561-578
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• 2010

This study proposes a multi-level damage localization strategy to achieve an effective damage detection system for civil infrastructure systems based on wireless sensors. The proposed system is designed for use of distributed computation in a wireless sensor network (WSN). Modal identification is achieved using the frequency-domain decomposition (FDD) method and the peak-picking technique. The ASH (angle-between-string-and-horizon) and AS (axial strain) flexibility-based methods are employed for identifying and localizing damage. Fundamentally, the multi-level damage localization strategy does not activate all of the sensor nodes in the network at once. Instead, relatively few sensors are used to perform coarse-grained damage localization; if damage is detected, only those sensors in the potentially damaged regions are incrementally added to the network to perform finer-grained damage localization. In this way, many nodes are able to remain asleep for part or all of the multi-level interrogations, and thus the total energy cost is reduced considerably. In addition, a novel distributed computing strategy is also proposed to reduce the energy consumed in a sensor node, which distributes modal identification and damage detection tasks across a WSN and only allows small amount of useful intermediate results to be transmitted wirelessly. Computations are first performed on each leaf node independently, and the aggregated information is transmitted to one cluster head in each cluster. A second stage of computations are performed on each cluster head, and the identified operational deflection shapes and natural frequencies are transmitted to the base station of the WSN. The damage indicators are extracted at the base station. The proposed strategy yields a WSN-based SHM system which can effectively and automatically identify and localize damage, and is efficient in energy usage. The proposed strategy is validated using two illustrative numerical simulations and experimental validation is performed using a cantilevered beam.

• ETRI Journal
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• v.43 no.2
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• pp.221-231
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• 2021

To resolve energy depletion issues in massive Internet of Things sensor networks, we developed a set of distributed energy beamforming methods with one-bit feedback and clustering for multi-node wireless energy transfer, where multiple singleantenna distributed energy transmitters (Txs) transfer their energy to multiple nodes wirelessly. Unlike previous works focusing on distributed information beamforming using a single energy receiver (Rx) node, we developed a distributed energy beamforming method for multiple Rx nodes. Additionally, we propose two clustering methods in which each Tx node chooses a suitable Rx node. Furthermore, we propose a fast distributed beamforming method based on Tx sub-clustering. Through computer simulations, we demonstrate that the proposed distributed beamforming method makes it possible to transfer wireless energy to massive numbers of sensors effectively and rapidly with small implementation complexity. We also analyze the energy harvesting outage probability of the proposed beamforming method, which provides insights into the design of wireless energy transfer networks with distributed beamforming.

• Journal of Sensor Science and Technology
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• v.31 no.1
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• pp.24-30
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• 2022

Distributed fiber-optic acoustic·vibration sensing technology is becoming increasingly popular in many industrial and academic areas such as in securing large edifices, exploring underground seismic activity, monitoring oil well/reservoir, etc. Long-range perimeter intrusion detection exemplifies an application that not only detects intrusion, but also pinpoints where it happens and recognizes kinds of threats made along the perimeter where a single fiber cable was installed. In this study, we developed a distributed fiber-optic sensing device that measures a distributed acoustic·vibration signature (pattern) for intrusion detection. In addition, we demontrate the proposed deep learning algorithm and how it classifies various intrusion events. We evaluated the sensing device and deep learning algorithm in a practical testbed setup. The evaluation results confirm that the developed system is a promising intrusion detection system for long-distance and seamless recognition requirements.

• Proceedings of the KSRS Conference
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• v.2
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• pp.768-771
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• 2006

In WSN(Wireless Sensor Network) environment, a large amount of sensors, which are small and heterogeneous, generates data stream successively in physical space. These sensors are composed of measured data and metadata. Metadata includes various features such as location, sampling time, measurement unit, and their types. Until now, wireless sensors have been managed with individual specification, not the explicit standardization of metadata, so it is difficult to collect and communicate between heterogeneous sensors. To solve this problem, OGC(Open Geospatial Consortium) has proposed a SensorML(Sensor Model Language) which can manage metadata of heterogeneous sensors with unique format. In this paper, we introduce a metadata model using SensorML specification to manage various sensors, which are distributed in a wide scope. In addition, we implement the metadata management module applied to the sensor data stream management system. We provide many functions, namely generating metadata file, registering and storing them according to definition of SensorML.