• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 2006.06a
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• pp.161-166
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• 2006

A new fiber orientation sensor has been developed and tested on an actual paper machine to demonstrate its capability to function as a real-time monitoring system. First, we demonstrate the ability of the sensor system to detect the change in the fiber orientation angle while the sensor head, and not the paper, was intentionally rotated from $-90^{\circ}\;to\;+70^{\circ}$ with respect to the paper-traveling direction. Next, we demonstrate that this system can successfully detect the change in the magnitude and angle of fiber orientation in running paper when the direction of material flow on the wire was changed on the paper machine. The angle and magnitude of fiber orientation were independently confirmed by SST and MOA measurements. Furthermore, we found that the system was capable of measuring the basis weight and the moisture content of running paper while detecting the angle and magnitude of fiber orientation.

• Proceedings of the Korean Society For Composite Materials Conference
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• 2003.04a
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• pp.125-128
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• 2003

Low-velocity impact damage is a major concern in the design of structures made of composite materials, because impact damage is hidden inside and cannot be detected by visual inspection. The piezoelectric thin film sensor can be used to detect variations in structural and material properties for structural health monitoring. In this paper, the PVDF and PZT sensors were used for monitoring impact damage initiation in Gr/Ep composite panel to illustrate this potential benefit. A series of impact test at various impact energy by changing impact mass and height is performed on the instrumented drop weight impact tester. The wavelet transform(WT) is used to decompose the piezoelectric sensor signals in this study. Test results show that the particular waveform of sensor signals implying the damage initiation and development are detected above the damage initiation impact energy. And it is found that both PZT and PVDF sensors can be used to detect the impact damage.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.766-768
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• 1998

Optical Temperature Distribution measurement System (OTDS) is completely different from conventional electric point sensor in that it uses the optical fiber itself as the sensor. This new concept in temperature measuring system requires only one fiber to be laid. The use of optical fiber also gives the advantage of small diameter, light weight, explosion resistance, and electromagnetic noise resistance. The OTDS is a sensor which is capable of making a precise measurement over a wide range of areas using only a single optical fiber. Since current temperature sensors, such as the thermocouple, are only used to measure temperaturea of point, they are almost impractical for measuring a wider range because of the extremely high cost. In comparision with current sensors, the optical fiber distributed temperature sensor can make much quicker and more precise measurements at a comparatively low cost.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2019.05a
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• pp.421-422
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• 2019

In this paper, we present the results of the implementation of the strain gauge sensor module which can be applied to measure the load weight in preparation for forklifts with high frequency of safety accidents in the industrial field. The sensor module consisted of strain gauge, LNA, LPF and ADC. In order to increase the accuracy and durability of the sensor, four sensors were inserted into four anchor bolts.

• Journal of the Korean Society for Nondestructive Testing
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• v.24 no.5
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• pp.499-507
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• 2004

We have studied tile detection techniques, which can determine the location and the weight of an intruder into infrastructure, by using fiber-optic ROTDR (Rayleigh optical time domain reflectometry) sensor and fiber-optic BOTDA (Brillouin Optical time domain analysis) sensor, which can use an optical fiber longer than that of ROTDR sensor Fiber-optic sensing plates of ROTDR sensor, which arc buried in sand, were prepared to respond the intruder effects. The signal of ROTDR was analyzed to confirm the detection performance. The constructed ROTDR could be used up to 10km at the pulse width of 30ns. The location error was less than 2 m and the weight could be detected as 4 grades, such as 20kgf, 40kgf, 60kgf and 80kgf. Also, fiber optic BOTDA sensor was developed to be able to detect intrusion effect through an optical fiber of tells of kilometers longer than ROTDR sensor. fiber-optic BOTDA sensor was constructed with 1 laser diode and 2 electro-optic modulators. The intrusion detection experiment was peformed by the strain inducing set-up installed on an optical table to simulate all intrusion effect. In the result of this experiment, the intrusion effort was well detected as the distance resolution of 3m through the fiber length of about 4.81km during 1.5 seconds.

• Journal of KIISE:Information Networking
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• v.35 no.1
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• pp.21-33
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• 2008

The wireless sensor nodes near to the fixed sink node suffer from the quickly exhausted battery energy. To address this problem, the mobile sink node has been applied to distribute the energy consumption into all wireless sensor nodes. However, since the mobile sink node moves, the data collection scheduling scheme is necessary for the sink node to receive the data from all sensor nodes as fair as possible. The application fields of wireless sensor network need the real-time processing. If the uneven data collection occurs in the wireless sensor network, the real-time processing for the urgent events can not be satisfied. In this paper, a new method is proposed to support the lair data collection between all sensor nodes. The proposed method performs the scheduling algorithm based on the resident time of the sink node staying in a radius of communication range and the amount of data transferred already. In this paper, the proposed method and existing data collection scheduling schemes are evaluated in wireless sensor network with the mobile sink node. The result shows that the proposed method provides the best fairness among all data collection schemes.

• Journal of Korea Spatial Information System Society
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• v.11 no.2
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• pp.27-33
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• 2009

The emergences of GeoSensor and researches about GIS have promoted many researches of u-GIS. The disaster application coupled in the u-GIS can apply to monitor accident area and to prevent spread of accident. The application needs the u-GIS DSMS technique to acquire, to process GeoSensor data and to integrate them with GIS data. The u-GIS DSMS must process big and large-volume data stream such as spatial data and multimedia data. Due to the feature of the data stream, in u-GIS DSMS, query processing can be delayed. Moreover, as increasing the input rate of data in the area generating events, the network traffic is increased. To solve this problem, in this paper we describe TRIGGER ACTION clause in CQ on the u-GIS DSMS environment and proposes data source management. Data source weight table controls GES information and incoming data rate. It controls incoming data rate as increasing weight at GES of disaster area. Consequently, it can contribute query processing rate and accuracy

• The KIPS Transactions:PartC
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• v.12C no.4 s.100
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• pp.519-524
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• 2005

Due to various types of errors added to dynamic weight measurement data, proper methods to reduce measurement errors are required to produce reliable weights. It is very difficult to reduce the measurement error due to excessive oscillation of the system. To cope with parasitic types of errors in real systems, information provided by the photo sensors are utilized and combined in such a way to reduce the measurement errors of load cells. In addition to four channels of load cells from a model trailer, photo sensors are used to obtain the information to compensate the error induced from vertical movement of the vehicle due to the variation of ground level. A model trailer system is run to verify the effectiveness of the proposed method to reduce noise of dynamic weight measurements.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1529-1547
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• 2015

Establishment of trust is important in wireless sensor networks for security enhancement and successful collaboration. Basically, a node establishes trust with other nodes by estimating a trust value based on monitored behavior of the other nodes. Since a malicious/misbehaving node might launch different attack strategies and might demonstrate random misbehavior, a trust estimation method should be robust against such attacks and misbehavior. Otherwise, the operation of trust establishment will be meaningless, and performance of an application that runs on top of trust establishment will degrade. In this paper, we propose a robust and novel trust estimation method. Unlike traditional trust estimation methods, we consider not only the weight of misbehavior but also the frequency of misbehavior. The frequency-of-misbehavior component explicitly demonstrates how frequently a node misbehaves during a certain observed time period, and it tracks the behavior of nodes more efficiently, which is a main factor in deriving an accurate trust value. In addition, the weight of misbehavior is comprehensively measured to mitigate the effect of an on-off attack. Frequency and weight of misbehavior are comprehensively combined to obtain the trust value. Evaluation results show that the proposed method outperforms other trust estimation methods under different attacks and types of misbehavior.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.8
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• pp.2259-2277
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• 2023

We propose a probabilistic fish growth model for smart aquaculture systems equipped with IoT sensors that monitor the ecological environment. As IoT sensors permeate into smart aquaculture systems, environmental data such as oxygen level and temperature are collected frequently and automatically. However, there still exists data on fish weight, tank allocation, and other factors that are collected less frequently and manually by human workers due to technological limitations. Unlike sensor data, human-collected data are hard to obtain and are prone to poor quality due to missing data and reading errors. In a situation where different types of data are mixed, it becomes challenging to develop an effective fish growth model. This study explores the unique characteristics of such a combined environmental and weight dataset. To address these characteristics, we develop a preprocessing method and a probabilistic fish growth model using mixed data sampling (MIDAS) and overlapping mixtures of Gaussian processes (OMGP). We modify the OMGP to be applicable to prediction by setting a proper prior distribution that utilizes the characteristic that the ratio of fish groups does not significantly change as they grow. We conduct a numerical study using the eel dataset collected from a real smart aquaculture system, which reveals the promising performance of our model.