• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 1992.03a
• /
• pp.83-102
• /
• 1992

As shear localization is observed in different deformation modes, an attempt is made to understand the conditions for shear localization in general deformation modes. Most emphasis in put upon the effects of pre-localization deformation mode on the onset of shear localization and all the other well-recognized effects of subtle constitutive features and imperfection sensitivity studied elsewhere are not investigated here. Rather, an approximate perturbation stability analysis is performed for simplified isotropic rigid-plastic solids subjected to general mode of homogeneous deformation. Shear localization is possible in any deformation mode if the material has strain softening. The incipient rate of shear localization and shear plane orientations are strongly dependent upon the pre-localization deformation mode. Significant strain softening is necessary for shear localization in homogeneous axisymmetric deformation modes while infinitesimal strain softening is necessary for shear localization in plane strain deformation mode. In any deformation mode, there are more than one shear plane orientation. Except for homogeneous axisymmetric deformation modes, there are two possible shear plane orientations with respect to the principal directions of stretching. Some well-known examples are discussed in the light of the current analysis.

• Journal of IKEEE
• /
• v.20 no.1
• /
• pp.1-8
• /
• 2016

In this paper, we propose an effective time-of-arrival (TOA)-based localization method with adaptive bias computation in indoor environments. The goal of the localization is to estimate an accurate target's location in wireless localization system. However, in indoor environments, non-line-of-sight (NLOS) errors block the signal propagation between target device and base station. The NLOS errors have significant effects on ranging between two devices for wireless localization. In TOA-based localization, finding the target's location inside the overlapped area in the TOA-circles is difficult. We present an effective localization method using compensated distance with adaptive bias computation. The proposed method is possible for the target's location to estimate an accurate location in the overlapped area using the measured distances with subtracted adaptive bias. Through localization experiments in indoor environments, estimation error is reduced comparing to the conventional localization methods.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.8 no.8
• /
• pp.2663-2674
• /
• 2014

For cognitive radio sensor networks (CRSNs) that use underlay-based spectrum access, the location of the primary user (PU) plays an important role in the power control of the secondary users (SUs), because the SUs must keep the minimum interference level required by the PU. Received signal strength (RSS)-based localization schemes provide low-cost implementation and low complexity, thus it is suitable for the PU localization in CRSNs. However, the RSS-based localization schemes have a high localization error because they use an inexact path loss exponent (PLE). Thus, applying a RSS-based localization scheme into the PU localization would cause a high interference to the PU. In order to reduce the localization error and improve the channel reuse rate, we propose a RSS-based PU localization scheme that uses distance calibration for CRSNs using underlay model-based spectrum access. Through the simulation results, it is shown that the proposed scheme can provide less localization error as well as more spectrum utilization than the RSS-based PU localization using the mean and the maximum likelihood calibration.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.3
• /
• pp.901-920
• /
• 2015

Wireless Sensor Networks (WSNs) are widely used in geographically isolated applications like military border area monitoring, battle field surveillance, forest fire detection systems, etc. Uninterrupted power supply is not possible in isolated locations and hence sensor nodes live on their own battery power. Localization of sensor nodes in isolated locations is important to identify the location of event for further actions. Existing localization algorithms consume more energy at sensor nodes for computation and communication thereby reduce the lifetime of entire WSNs. Existing approaches also suffer with less localization coverage and localization accuracy. The objective of the proposed work is to increase the lifetime of WSNs while increasing the localization coverage and localization accuracy. A novel intelligent unmanned aerial vehicle anchor node (IUAN) is proposed to reduce the communication cost at sensor nodes during localization. Further, the localization computation cost is reduced at each sensor node by the proposed intelligent arc selection (IAS) algorithm. IUANs construct the location-distance messages (LDMs) for sensor nodes deployed in isolated locations and reach the Control Station (CS). Further, the CS aggregates the LDMs from different IUANs and computes the position of sensor nodes using IAS algorithm. The life time of WSN is analyzed in this paper to prove the efficiency of the proposed localization approach. The proposed localization approach considerably extends the lifetime of WSNs, localization coverage and localization accuracy in isolated environments.

• The Journal of Korea Robotics Society
• /
• v.16 no.4
• /
• pp.306-312
• /
• 2021

This paper describes localization of autonomous underwater vehicles(AUV), which can be used when some navigation sensor data are an outlier. In that situation, localization through existing navigation algorithms causes problems in long-range localization. Even if an outlier sensor data occurs once, problems of localization will continue. Also, if outlier sensor data is related to azimuth (direction of AUV), it causes bigger problems. Therefore, a parallel localization module, in which different algorithms are performed in a normal and abnormal situation should be designed. Before designing a parallel localization module, it is necessary to study an effective method in the abnormal situation. So, we propose a localization method through machine learning. For this method, a learning model consists of only Fully-Connected and trains through randomly contaminated real sea data. The ground truth of training is displacement between subsequent GPS data. As a result, average error in localization through the learning model is 0.4 times smaller than the average error in localization through the existing navigation algorithm. Through this result, we conclude that it is suitable for a component of the parallel localization module.

• Proceedings of the Korea Concrete Institute Conference
• /
• 1998.04a
• /
• pp.353-358
• /
• 1998

The strain localization is a discontinuous phenomenon that addresses the formation of jumps of the field variables across a singularity surface. It has become widely accepted that the localization may occur as the result of discontinuous bifurcation which corresponds to the loss of ellipticity of the governing differential equations for elasto-plastic continua. In this paper, condition for strain localization in concrete based on bifurcation theory is studied and localization tensor analysis algorithm is employed to determine the directions of localization of deformations in concrete. By applying a plasticity model of concrete into the algorithm, localization analysis is performed concrete under uniaxial tension, pure shear and uniaxial compression.

• 제어로봇시스템학회:학술대회논문집
• /
• 2002.10a
• /
• pp.92.3-92
• /
• 2002

A lot of localization algorithms have been developed in order to achieve autonomous navigation. However, most of localization algorithms are restricted to certain conditions. In this paper, Monte Carlo localization scheme with a map-matching algorithm is suggested as a robust localization method for the Public Service Robot to accomplish its tasks autonomously. Monte Carlo localization can be applied to local, global and kidnapping localization problems. A range image based measure function and a geometric pattern matching measure function are applied for map matching algorithm. This map matching method can be applied to both polygonal environments and un-polygonal environments and achieves...

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2020.07a
• /
• pp.717-720
• /
• 2020

An indoor localization system that uses Wi-Fi RSSI signals for localization gives accurate user position results. The conventional Wi-Fi RSSI signal based localization system uses raw RSSI signals from access points (APs) to estimate the user position. However, the RSSI values of a particular location are usually not stable due to the signal propagation in the indoor environments. To reduce the RSSI signal fluctuations, shadow fading, multipath effects and the blockage of Wi-Fi RSSI signals, we propose a Wi-Fi localization system that utilizes the advantages of Wi-Fi RSSI heat maps. The proposed localization system uses a regression model with deep convolutional neural networks (DCNNs) and gives accurate user position results for indoor localization. The experiment results demonstrate the superior performance of the proposed localization system for indoor localization.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.5
• /
• pp.433-439
• /
• 2010

This paper proposes a beacon color code scheduling algorithm for the localization of multiple robots in a multi-block workspace. With the developments of intelligent robotics and ubiquitous technology, service robots are applicable for the wide area such as airports and train stations where multiple indoor GPS systems are required for the localization of the mobile robots. Indoor localization schemes using ultrasonic sensors have been widely studied due to its cheap price and high accuracy. However, ultrasonic sensors have some shortages of short transmission range and interferences with other ultrasonic signals. In order to use multiple robots in wide workspace concurrently, it is necessary to resolve the interference problem among the multiple robots in the localization process. This paper proposes an indoor localization system for concurrent multiple robots localization in a wide service area which is divided into multi-block for the reliable sensor operation. The beacon color code scheduling algorithm is developed to avoid the signal interferences and to achieve efficient localization with high accuracy and short sampling time. The performance of the proposed localization system is verified through the simulations and the real experiments.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.5
• /
• pp.415-419
• /
• 2010

A variety of methods for sound source localization have been developed and applied to several applications such as noise detection system, surveillance system, teleconference system, robot auditory system and so on. In the previous work, we proposed the sound source localization using the spatially mapped GCC functions based on TDOA for robot auditory system. Performance of the proposed one for the noise effect and estimation resolution was verified with the real environmental experiment under the single source assumption. However, since multi-talker case is general in human-robot interaction, multiple source localization approaches are necessary. In this paper, the proposed localization method under the single source assumption is modified to be suitable for multiple source localization. When there are two sources which are correlated, the spatially mapped GCC function for localization has three peaks at the real source locations and imaginary source location. However if two sources are uncorrelated, that has only two peaks at the real source positions. Using these characteristics, we modify the proposed localization method for the multiple source cases. Experiments with human speeches in the real environment are carried out to evaluate the performance of the proposed method for multiple source localization. In the experiments, mean value of estimation error is about $1.4^{\circ}$ and percentage of multiple source localization is about 62% on average.