• Journal of the Institute of Convergence Signal Processing
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• v.12 no.2
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• pp.113-118
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• 2011

In this paper, we develop a speaker gender classification technique using collaborative sensor fusion for use in a wireless sensor network. The distributed sensor nodes remove the unwanted input data using the BER(Band Energy Ration) based voice activity detection, process only the relevant data, and transmit the hard labeled decisions to the fusion center where a global decision fusion is carried out. This takes advantages of power consumption and network resource management. The Bayesian sensor fusion and the global weighting decision fusion methods are proposed to achieve the gender classification. As the number of the sensor nodes varies, the Bayesian sensor fusion yields the best classification accuracy using the optimal operating points of the ROC(Receiver Operating Characteristic) curves_ For the weights used in the global decision fusion, the BER and MCL(Mutual Confidence Level) are employed to effectively combined at the fusion center. The simulation results show that as the number of the sensor nodes increases, the classification accuracy was even more improved in the low SNR(Signal to Noise Ration) condition.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.5
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• pp.577-584
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• 2009

Fusion of decisions in wireless sensor networks having flexibility on energy efficiency is studied in this paper. Two representative distributions, the generalized Gaussian and $\alpha$-stable probability density functions, are used to model non-Gaussian noise channels. By incorporating noise channels into the parallel fusion model, the optimal fusion rules are represented and suboptimal fusion rules are derived by using a large signal-to-noise ratio(SNR) approximation. For both distributions, the obtained suboptimal fusion rules are same and have equivalent form to the Chair-Varshney fusion rule(CVR). Thus, the CVR does not depend on the behavior of noise distributions that belong to the generalized Gaussian and $\alpha$-stable probability density functions. The simulation results show the suboptimality of the CVR at large SNRs.

• Journal of the Korean Society for Precision Engineering
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• v.20 no.11
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• pp.79-90
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• 2003

In this paper, we propose a multisensor-based navigation algorithm for a mobile robot, which is intelligently searching the goal location in unknown dynamic environments using multi-ultrasonic sensor. Instead of using “sensor fusion” method which generates the trajectory of a robot based upon the environment model and sensory data, “command fusion” method by fuzzy inference is used to govern the robot motions. The major factors for robot navigation are represented as a cost function. Using the data of the robot states and the environment, the weight value of each factor using fuzzy inference is determined for an optimal trajectory in dynamic environments. For the evaluation of the proposed algorithm, we performed simulations in PC as well as experiments with IRL-2002. The results show that the proposed algorithm is apt to identify obstacles in unknown environments to guide the robot to the goal location safely.

• Journal of the Institute of Convergence Signal Processing
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• v.14 no.4
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• pp.243-248
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• 2013

In wireless sensor networks, consensus algorithms for dynamic systems may flexibly usable for their data fusion of a sensor network. In this paper, a distributed data fusion filter is implemented using an average consensus based on distributed sensor data, which is composed of some sensor nodes and a sink node to track the mean values of n sensors' data. The consensus filter resolve the problem of data fusion by a distribution Kalman filtering scheme. We showed that the consensus filter has an optimal convergence to decrease of noise propagation and fast tracking ability for input signals. In order to verify for the results of consensus filtering, we showed the output signals of sensor nodes and their filtering results, and then showed the result of the combined signal and the consensus filtering using zeegbee communication.

• Smart Structures and Systems
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• v.7 no.3
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• pp.229-240
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• 2011

Recent developments in Smart Structures with very large scale embedded sensors and actuators have introduced new challenges in terms of data processing and sensor fusion. These smart structures are dynamically classified as a large-scale system with thousands of sensors and actuators that form the musculoskeletal of the structure, analogous to human body. In order to develop structural health monitoring and diagnostics with data provided by thousands of sensors, new sensor informatics has to be developed. The focus of our on-going research is to develop techniques and algorithms that would utilize this musculoskeletal system effectively; thus creating the intelligence for such a large-scale autonomous structure. To achieve this level of intelligence, three major research tasks are being conducted: development of a Bio-Inspired data analysis and information extraction from thousands of sensors; development of an analytical technique for Optimal Sensory System using Structural Observability; and creation of a bio-inspired decision-making and control system. This paper is focused on the results of our effort on the first task, namely development of a Neuro-Morphic Engineering approach, using a neuro-symbolic data manipulation, inspired by the understanding of human information processing architecture, for sensor fusion and structural diagnostics.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.16-21
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• 2005

This paper considers the problem of state estimation in linear continuous-time systems with multi-sensor environment and observation uncertainties. We propose two suboptimal filtering algorithms for these types of systems. The filtering algorithms consist of two steps: The local optimal Kalman estimates are computed at the first step. And, these local estimates are lineally fused at the second step. The implementation of the two-step filtering algorithms needs a lower memory demand than the optimal Kalman and adaptive Lainiotis-Kalman filters. In consequence of parallel structure of the proposed filters, the parallel computers can be used for their design. The examples exhibit the effect of common noise on the performance of fusion of the local Kalman estimates based on observations from different sensors and in the presence of uncertainties.

• Korean Journal of Remote Sensing
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• v.36 no.5_1
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• pp.807-821
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• 2020

The objective of this study is to evaluate the applicability of representative spatio-temporal fusion models developed for the fusion of mid- and low-resolution satellite images in order to construct a set of time-series high-resolution images for crop monitoring. Particularly, the effects of the characteristics of input image pairs on the prediction performance are investigated by considering the principle of spatio-temporal fusion. An experiment on the fusion of multi-temporal Sentinel-2 and RapidEye images in agricultural fields was conducted to evaluate the prediction performance. Three representative fusion models, including Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), SParse-representation-based SpatioTemporal reflectance Fusion Model (SPSTFM), and Flexible Spatiotemporal DAta Fusion (FSDAF), were applied to this comparative experiment. The three spatio-temporal fusion models exhibited different prediction performance in terms of prediction errors and spatial similarity. However, regardless of the model types, the correlation between coarse resolution images acquired on the pair dates and the prediction date was more significant than the difference between the pair dates and the prediction date to improve the prediction performance. In addition, using vegetation index as input for spatio-temporal fusion showed better prediction performance by alleviating error propagation problems, compared with using fused reflectance values in the calculation of vegetation index. These experimental results can be used as basic information for both the selection of optimal image pairs and input types, and the development of an advanced model in spatio-temporal fusion for crop monitoring.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.8
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• pp.807-813
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• 2011

High precision of planning in the preoperative phase can contribute to increase operational safety during computer-aided spinal fusion surgery, which requires extreme caution on the part of the surgeon, due to the complexity and delicacy of the procedure. In this paper, an advanced preoperative planning framework for spinal fusion is presented. The framework is based on spinal pedicle data obtained from CT (Computed Tomography) images, and provides optimal insertion trajectories and pedicle screw sizes. The proposed approach begins with safety margin estimation for each potential insertion trajectory that passes through the pedicle volume, followed by procedures to collect a set of insertion trajectories that satisfy operation safety objectives. The radius of a pedicle screw was chosen as 70% of the pedicle radius. This framework has been tested on 68 spinal pedicles of 8 patients requiring spinal fusion. It was successfully applied, resulting in an average success rate of 100% and a final safety margin of $2.44{\pm}0.51mm$.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.6 no.1
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• pp.287-304
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• 2012

Spectrum sensing is an important functionality for cognitive users to look for spectrum holes before taking transmission in dynamic spectrum access model. Unlike previous works that assume perfect knowledge of the SNR of the signal received from the primary user, in this paper we consider a realistic case where the SNR of the primary user's signal is unknown to both fusion center and cognitive radio terminals. A Kalman filter based adaptive Takagi and Sugeno's fuzzy system is designed to make the global spectrum sensing decision based on the observed energies from cognitive users. With the capacity of adapting system parameters, the fusion center can make a global sensing decision reliably without any requirement of channel state information, prior knowledge and prior probabilities of the primary user's signal. Numerical results prove that the sensing performance of the proposed scheme outperforms the performance of the equal gain combination based scheme, and matches the performance of the optimal soft combination scheme.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.29 no.11
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• pp.96-101
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• 2015

In recent years, researches regarding optimal placement of CCTV(Closed-circuit Television) cameras via architecture modeling has been conducted. However, for analyzing surveillance coverage through actual human movement, the application of VA(Video Analytics) function of IP(Internet Protocol) cameras has not been studied. This paper compares two methods using data captured from real-world cameras and data acquired from a virtual environment. In using real cameras, we develop GUI(Graphical User Interface) to be used as a logfile which is stored hourly and daily through VA functions and to be used commercially for placement of products inside a shop. The virtual environment was constructed to emulate an real world such as the building structure and the camera with its specifications. Moreover, suitable placement of the camera is done by recognizing obstacles and the number of people counted within the camera's range of view. This research aims to solve time and economic constraints of actual installation of surveillance cameras in real-world environment and to do feasibility study of virtual environment.