Chang Ho Yu

Distributed single target tracking in underwater wireless sensor networks

The growing interests in underwater wireless sensor networks(UWSNs) for a wide variety of purposes from Tsunami monitoring to commercial oilfield exploration projects have been encouraging. This paper deals with the problem of accurately tracking a single target moving through UWSNs employing acoustic sensors. This paper addresses the issues of estimating the target position, improving energy efficiency by applying a Kalman filter in a distributed architecture. Each underwater wireless sensor nodes composing the UWSNs is battery-powered, so the energy conservation problem is a critical issue. This paper provides an algorithm which increases energy efficiency of each sensor through a wake-up/sleep(WuS) and a valid measurement selecting(VMS) scheme. Simulation results illustrate the performance of the tracking filter according to the sensor node displacement and sensor detecting area.

ToA based sensor localization in underwater wireless sensor networks

Currently several kinds of sensor localization methods have been developed for terrestrial wireless sensor networks. In this paper, in order to extend the field to underwater environments, we study sensor localization technique for underwater wireless sensor networks(UWSNs). In underwater environments, radio frequency(RF) signal is not suitable for underwater usage because of extremely limited propagation. Because of that reason UWSNs should be constituted with acoustic modems. So we need a new localization algorithm to determine each sensors position. First of all we study the localization techniques for terrestrial environments where we investigate possible methods to underwater environment. And then we present the appropriate algorithm in the underwater usage. Finally we, evaluate the underwater based localization algorithm using computer based different conditions among communication range of sensor node, the nodes number and position of reference node.

Sensor localization-based distributed target tracking filter in underwater sensor networks

This paper deals with the problem of localizing sensors based on time of arrival (ToA) method and tracking a target moving through the environment of underwater sensor networks (USNs). ToA method for localizing sensors is the most simple and intuitive algorithm applicable for USNs. In this paper, ToA-based sensor localization system in USNs is proposed, which consists of ToA-based distance estimation, position computation using trilateration, and localization algorithm using recursive position estimation (RPE). By using the localized underwater sensor information, distributed Kalman filter (DKF) can estimate the state of target on valid sensor platforms. Simulation results illustrate the performance of the proposed distributed tracking filters.

An Overload Detecting Method for an Excavator Based on the Correlation Function

In this paper, an overload detecting algorithm for an excavator is presented. The proposed overload detecting algorithm is based on the time series analysis especially correlation function. The main purpose of this paper is to prevent damage or crack from the fatigue loaded on an excavator in advance. Generally, the larger data, the longer processing time, and the amount of the data used in this paper are also large, especially every sampling period, 1600 data are gathered and calculated. So this paper focuses on minimizing the number of required sensors by using the correlation function. From the cross correlation function, similar pattern sensors are eliminated and dissimilar pattern sensors are considered, and from the auto correlation function, the overload can be detected. To prove the efficiency of the proposed overload detecting algorithm, this paper shows the computer simulation results.

Time series analysis based overload detection algorithm for excavator

In this paper, an overload detecting algorithm for an excavator is presented. The proposed overload detecting algorithm is based on the time series analysis especially moving window method and correlation function. The main purpose of this paper is to prevent damage or crack from the fatigue in advance. In this paper 16 channel sensor data are considered and each sensor frequency is 100 Hz and sampling period is lsec. So every sampling period 1600 data are gathered and computed, and the larger data, the longer process time. So this paper focuses on 2 topics. One is to short the process time. The other is to minimize the number of required sensors. To short the process time, this paper uses the moving window method. From the moving window method only data within each moving window are considered, so process time and process burden is shortened. And to minimize the number of required sensors, this paper uses the correlation function. From cross correlation function similar pattern sensors are eliminated and dissimilar pattern sensors are considered. And from using auto correlation function each dissimilar pattern sensor data are investigated to check overload or not. To prove the efficiency of the proposed overload detecting algorithm, this paper shows the computer simulation results.