• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.205-210
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• 2017

Wireless sensor networks are useful to various unmanned monitoring application such as monitoring environments, surveillance system, unmanned space exploration, and so on. Due to the inappropriate placement of sensor nodes, there are some problems, for example, low connectivity and high overlapped sensing area. These problems can make it difficult for the data collection and lead to a waste of energy. In this paper, we propose a node relocating method to resolve the inappropriate placement of sensor nodes. Given monitoring area, we place sensor nodes randomly and find redundant nodes and move them to uncovered area. Through the simulation, We show that the proposed method is viable and efficient compared with the existing randomly locating method.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.1201-1211
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• 2015

WSN acts as an effective tool for tracking the large scale environments. In such environment, the battery life of the sensor networks is limited due to collection of the data, usage of sensing, computation and communication. To resolve this, a mobile robot is presented to identify the data present in the partitioned sensor networks and passed onto the sink. In novel data collection algorithm, the performance of the data collecting operation is reduced because mobile robot can be used only within the limited range. To enhance the data collection in a changing environment, Non Linear Error Identifier (NLEI) algorithm has been developed and presented in this paper to configure the robot by means of error models which are non-linear. Experimental evaluation has been conducted to estimate the performance of the proposed NLEI and it has been observed that the proposed NLEI algorithm increases the error correction rate upto 42% and efficiency upto 60%.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.69-76
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• 2019

Microheaters with different structures were fabricated and compared to find an optimal configuration enhancing the performances of $C_2H_2$ gas sensor. Three temperature sensors were integrated on the surface of the insulation layer over the microheater, and resistance changes were observed to check the generated heat from the microheater. A low operating voltage of 1mV was applied to the temperature sensor to minimize any influence of thermal heat from the resistance type temperature sensor, whereas high voltages in the range between 10 and 20V were applied to the microheater. A microheater structure generating maximum heat at low voltage was determined. The generated heat was verified by the temperature sensors on the top of the $Si_3N_4$ and infrared camera. A long term stability and accuracy of the microheater were observed. The developed microheater was applied to enhance the performances of $C_2H_2$ gas sensor and successfully confirmed that the developed microheater greatly contributes to the improvement of sensitivity and selectivity of gas sensor.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.336-339
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• 2007

A sensor having capability to measure two different kinds of ultrasonic waves selectively is described. Under the same bias field configuration, this magnetostrictive type sensor can selectively measure longitudinal waves and flexural ones. Since the switching operation of mode selection is made only by changing the polarity of the permanent magnets used to configure the bias field, it will be very useful after the permanent magnet are interchanged with electromagnets. In order to find the optimal operating bias field, finite elements analysis is used and the condition for flexural wave measurements to meet is found. The linearity of the sensor is verified by experiments and the requirements for linearity also are proposed.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.568-573
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• 2007

Environmental information (temperature, humidity, vibration, $CO_2$, gas leakage, etc.) of building is an essential item to manage and monitor a building. For intelligent building, it is necessary to get temperature and illumination information to save energy and crack information to prevent structural problems. Moreover, temperature and gas leakage information to alarm a tire precaution, or humidity information to maintain comfortable environment. However, there have not been many researches on systems for gathering environmental information and building maintenance due to high costs. In this paper, wireless sensor network technology is applied to collecting building environmental information. Wireless sensor network is one of the latest issues and has low-power consumption, low-cost, self-configuration features.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2011.10a
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• pp.820-823
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• 2011

WSN is composed of a lot of small sensors with the limited hardware resources. In WSN, at the initial stage, sensor nodes are randomly deployed over the region of interest, and self-configure the clustered networks by grouping a bunch of sensor nodes and selecting a cluster header among them. In this paper, we propose a self-configuration routing protocol for WSN, which consists of step-wise ripple routing algorithm for initial deployment, effective joining of sensor nodes. RRA is search node in RF-coverage of each node, which result in fast network connection, reducing overall power consumption, and extending the lifetime of network.

• Smart Structures and Systems
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• v.10 no.3
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• pp.209-228
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• 2012

Structural health monitoring with wireless sensor networks has received much attention in recent years due to the ease of sensor installation and low deployment and maintenance costs. However, sensor network technology needs to solve numerous challenges in order to substitute conventional systems: large amounts of data, remote configuration of measurement parameters, on-site calibration of sensors and robust networking functionality for long-term deployments. We present a structural health monitoring network that addresses these challenges and is used in several deployments for monitoring of bridges and buildings. Our system supports a diverse set of sensors, a library of highly optimized processing algorithms and a lightweight solution to support a wide range of network runtime configurations. This allows flexible partitioning of the application between the sensor network and the backend software. We present an analysis of this partitioning and evaluate the performance of our system in three experimental network deployments on civil structures.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.11
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• pp.1102-1107
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• 2009

An ultrasonic sensor has been widely used as a range sensor for its low cost and capability of detecting some obstacles, such as glasses and black surfaces, which are not well detected by a laser scanner and an IR sensor. Although low-cost sensors are preferred for practical service robots, they suffer from the inaccurate and insufficient range information. This paper proposes a novel approach to obstacle avoidance using low-cost anisotropic ultrasonic sensors with wide beam angle. In this paper, obstacles can be detected by the proposed sensor configuration which consists of one transmitter and three receivers. Because even wide obstacles are represented by a point, which corresponds to the intersection of range data from each receiver of the anisotropic sensor, a robot cannot avoid wide obstacles successfully. This paper exploits the probabilistic mapping technique to avoid collision with various types of obstacles. The experimental results show that the proposed method can robustly avoid obstacles in most indoor environments.

• Journal of Korea Multimedia Society
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• v.16 no.10
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• pp.1196-1203
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• 2013

In general, the cost of transceiver for wireless network configuration is more expensive than that for wired network. However, in case of environmental management system in a tunnel, the cost can be minimized by adopting low rate tranceiver because the amount of the exchanged data for tunnel monitoring is very small. When the obtained data from sensor node is sent directly to the corresponding command node, there is no need to consider routing problem of the data transfer. However in this case, sensor nodes are required to be implemented with high power transmitter and experience high energy consumption. To tackle this problem, relay nodes can be used to transfer the data of tunnel monitoring, and suitable routing protocols for selecting optimum path are needed. Therefore, in this paper, we propose a routing algorithm and a self-configuration protocol for environment management system in tunnel.

• Structural Monitoring and Maintenance
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• v.3 no.3
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• pp.297-314
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• 2016

As the study of internal corrosion of pipeline need a large number of experiments as well as long time, so there is a need for new computational technique to expand the spectrum of the results and to save time. The present work represents a new non-destructive evaluation (NDE) technique for detecting the internal corrosion inside pipeline by evaluating the dielectric properties of steel pipe at room temperature by using electrical capacitance sensor (ECS), then predict the effect of pipeline environment temperature (${\theta}$) on the corrosion rates by designing an efficient artificial neural network (ANN) architecture. ECS consists of number of electrodes mounted on the outer surface of pipeline, the sensor shape, electrode configuration, and the number of electrodes that comprise three key elements of two dimensional capacitance sensors are illustrated. The variation in the dielectric signatures was employed to design electrical capacitance sensor (ECS) with high sensitivity to detect such defects. The rules of 24-electrode sensor parameters such as capacitance, capacitance change, and change rate of capacitance are discussed by ANSYS and MATLAB, which are combined to simulate sensor characteristic. A feed-forward neural network (FFNN) structure are applied, trained and tested to predict the finite element (FE) results of corrosion rates under room temperature, and then used the trained FFNN to predict corrosion rates at different temperature using MATLAB neural network toolbox. The FE results are in excellent agreement with an FFNN results, thus validating the accuracy and reliability of the proposed technique and leads to better understanding of the corrosion mechanism under different pipeline environmental temperature.