• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1051-1059
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• 2014

In this paper, we propose an intelligent situation recognition model by collecting and analyzing multiple sensor signals. Multiple sensor signals are collected for fixed time window. A training set of collected sensor data for each situation is provided to K2-learning algorithm to generate Bayesian networks representing causal relationship between sensors for the situation. Statistical characteristics of sensor values and topological characteristics of generated graphs are learned for each situation. A neural network is designed to classify the current situation based on the extracted features from collected multiple sensor values. The proposed method is implemented and tested with UCI machine learning repository data.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.38 no.8
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• pp.27-33
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• 2001

The performance of the ordinary Fiber Bragg Grating(FBG) sensor strain measurement system, which uses Fabry-Perot filter for scanning wavelength, has limitation for application because of hysteresis characteristics of PZT element in the filter, slow scan rate of the filter and the high cost of system. We proposed and experimented a multiple FBG sensor system using light emitting diode(LED) as a light source and adapting Code Division Multiplexing(CDM) method to separating out individual sensor signal. Output signals for a applied static and dynamic strain and crosstalk levels between sensor signals were measured. The price of the system is very loss and the response speed is very fast. Crosstalk levels between sensor signals below - 30 dB were demonstrated.

• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.1
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• pp.97-105
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• 2009

In this paper, the performance of AP(Alternating Projection) and EM(Expectation Maximization) algorithms is investigated in terms of detection of multiple signals, resolvability of coherent signals and the efficiency of sensor array processing. The basic idea of these algorithms is utilization of relaxation technique of successive 1D maximization to solve a direction finding problem by maximizing the multidimensional likelihood function. It means that the function is maximized over only for a single parameter while the other parameters are fixed at each step of the iteration. According to simulation results, the algorithms showed good performance for both incoherent and coherent multiple signals. Moreover, some advantages are identified for direction finding with very small samples and fast convergence. The performance of AP algorithm is compared with that of EM using multiple criteria such as the number of sensor, SNR, the number of samples, and convergence speed over uniform circular array. It is resulted AP algorithm is superior to EM overally except for one criterion, convergence speed. Especially, for EM algorithm there is no performance difference between incoherent and coherent case. In conclusion, AP and EM are viable and practical alternatives, which can be applied to a direction under due to the resolvability of multi-path signals, reliable performance and no troublesome eigen-decomposition of the sample-covariance matrix.

• Journal of electromagnetic engineering and science
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• v.18 no.4
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• pp.254-260
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• 2018

In this study, we suggest an emulator for generating multiple heterogeneous interference signals in the Korean radio frequency identification/ubiquitous sensor network (RFID/USN) frequency band. The proposed emulator uses only one universal software radio peripheral to generate multiple heterogeneous interference signals more economically. Moreover, the physical and media access control parameters can be adjusted in real time using the LabVIEW program, thereby making it possible to create various time-varying interference environments easily. As an example showing the capability of the proposed emulator, multiple interference signals consisting of a frequency-hopping RFID signal and two LoRa signals with different spreading factors were generated. The generated signals were confirmed in both frequency and time domains. From the experimental results, we verified that our emulator could successfully generate multiple heterogeneous interference signals with different frequency and time domain characteristics.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1287-1300
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• 2018

Target-tracking algorithm based on ad hoc camera sensor networks (ACSNs) utilizes the distributed observation capability of nodes to achieve accurate target tracking. A compressed sensing-based multiple-target tracking algorithm (CSMTTA) for ACSNs is proposed in this work based on the study of camera node observation projection model and compressed sensing model. The proposed algorithm includes reconfiguration of observed signals and evaluation of target locations. It reconfigures observed signals by solving the convex optimization of L1-norm least and forecasts node group to evaluate a target location by the motion features of the target. Simulation results show that CSMTTA can recover the subtracted observation information accurately under the condition of sparse sampling to a high target-tracking accuracy and accomplish the distributed tracking task of multiple mobile targets.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.2519-2523
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• 2003

Ultrasonic sensors are widely used in mobile robot applications to recognize external environments, because they are cheap, easy to use, and robust under varying lighting conditions. In most cases, a single ultrasonic sensor is used to measure the distance to an object based on time-of-flight (TOF) information, whereas multiple sensors are used to recognize the shape of an object, such as a corner, plane, or edge. However, the conventional sequential driving technique involves a long measurement time. This problem can be resolved by pulse coding ultrasonic signals, which allows multi-sensors to be fired simultaneously and adjacent objects to be distinguished. Accordingly, the current presents a new simultaneous coded driving system for an ultrasonic sensor array for object recognition in autonomous mobile robots. The proposed system is designed and implemented using a DSP and FPGA. A micro-controller board is made using a DSP, Polaroid 6500 ranging modules are modified for firing the coded signals, and a 5-channel coded signal generating board is made using a FPGA. To verify the proposed method, experiments were conducted in an environment with overlapping signals, and the flight distances for each sensor were obtained from the received overlapping signals using correlations and conversion to a bipolar PCM-NRZ signal.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.11
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• pp.1028-1036
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• 2004

Ultrasonic sensors are widely used in mobile robot applications to recognize external environments by virtue that they are cheap, easy to use, and robust under varying lighting conditions. In most cases, a single ultrasonic sensor is used to measure the distance to an object based on time-of-flight (TOF) information, whereas multiple sensors are used to recognize the shape of an object, such as a comer, plane, or edge. However, the conventional sequential driving technique involves a long measurement time. This problem can be resolved by pulse coding of ultrasonic signals, which allows multi-sensors to be emitted simultaneously and adjacent objects to be distinguished. Accordingly, this paper presents a new simultaneous coded driving system for an ultrasonic sensor array for object recognition in autonomous mobile robots. The proposed system is designed and implemented. A micro-controller unit is implemented using a DSP, Polaroid 6500 ranging modules are modified for firing the coded signals, and a 5-channel coded signal generating board is made using a FPGA. To verify the proposed method, experiments were conducted in an environment with overlapping signals, and the flight distances fur each sensor were obtained from the received overlapping signals using correlations and conversion to a bipolar PCM-NRZ signal.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.44 no.3
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• pp.86-97
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• 2021

As mechatronic systems have various, complex functions and require high performance, automatic fault detection is necessary for secure operation in manufacturing processes. For conducting automatic and real-time fault detection in modern mechatronic systems, multiple sensor signals are collected by internet of things technologies. Since traditional statistical control charts or machine learning approaches show significant results with unified and solid density models under normal operating states but they have limitations with scattered signal models under normal states, many pattern extraction and matching approaches have been paid attention. Signal discretization-based pattern extraction methods are one of popular signal analyses, which reduce the size of the given datasets as much as possible as well as highlight significant and inherent signal behaviors. Since general pattern extraction methods are usually conducted with a fixed size of time segmentation, they can easily cut off significant behaviors, and consequently the performance of the extracted fault patterns will be reduced. In this regard, adjustable time segmentation is proposed to extract much meaningful fault patterns in multiple sensor signals. By considering inflection points of signals, we determine the optimal cut-points of time segments in each sensor signal. In addition, to clarify the inflection points, we apply Savitzky-golay filter to the original datasets. To validate and verify the performance of the proposed segmentation, the dataset collected from an aircraft engine (provided by NASA prognostics center) is used to fault pattern extraction. As a result, the proposed adjustable time segmentation shows better performance in fault pattern extraction.

• Journal of the Korean Society for Precision Engineering
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• v.12 no.12
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• pp.100-108
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• 1995

The In-process minitoring of the chatter vibration is necessarily required to an automatic manufacturing system. In this study, we constructed a multi-sensing system using tool dynamoneter, accelerometer and AE(Acoustic Emission) sensor for a more credible detection of chatter vibration. And a new approach using a multiple neural network to extract the features of multi-sensor for the recognition chatter vibration is proposed. With the Back-propagation training process, the neural network memorize and classify the features of multi-sensor signals. As a result, it is shown by multiple neural network that the chatter vibration can be monitored accurately, and it can be widely used in practical unmanned system.

• Proceedings of the KIEE Conference
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• 1998.11c
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• pp.764-766
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• 1998

An evaluation method for oxygen permeable characteristics of the membrane covering to each cathode of multiple cathode - single anode type dissolved oxygen sensor, which has high reproducibility and is capable of measuring multiple components in solutions. For this purpose, a measuring circuit for the multiple cathode type DO sensor was designed to lower the noise signal by adapting a digital LPF to readout the sensor output accurately. Digital LPF is designed by setting up the transfer function to set the cutoff frequency to 10Hz, and the transfer function is programmed by C language, and then the filtering characteristics are evaluated with the simulation and experiments. Using this LPF added measuring circuit for the multiple cathode type DO sensor, we have obtained the calibration factor for each cathode to calibrate the variation of the output signals. The calibration factor was obtained by measuring the sensor output signal followed by oxygen partial pressure, using the same oxygen permeable membrane at each cathode of the multiple cathode type DO sensor.