• Journal of the Korean Society of Manufacturing Process Engineers
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• v.19 no.1
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• pp.63-71
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• 2020

Recently, to improve convenience, flexible electronics are quickly being developed for a number of application areas. Flexible electronic devices comprise characters such as being bendable, stretchable, foldable, and wearable. Effectively manufacturing flexible electronic devices requires high efficiency, low costs, and simple processes for manufacturing technology. Through this study, we enabled the rapid production of multifunctional flexible bending sensors using a simple, low-cost Fused Deposition Modeling (FDM) 3D printer. Furthermore, we demonstrated the possibility of the rapid production of a range of functional flexible bending sensors using a simple, low-cost FDM 3D printer. Accurate and reproducible functional materials made by FDM 3D printers are an effective tool for the fabrication of flexible sensor electronic devices. The 3D-printed flexible bending sensor consisted of polyurethane and a conductive filament. Two patterns of electrodes (straight and Hilbert curve) for the 3D printing flexible sensor were fabricated and analyzed for the characteristics of bending displacement. The experimental results showed that the straight curve electrode sensor sensing ability was superior to the Hilbert curve electrode sensor, and the electrical conductivity of the Hilbert curve electrode sensor is better than the straight curve electrode sensor. The results of this study will be very useful for the fabrication of various 3D-printed flexible sensor devices with multiple degrees of freedom that are not limited by size and shape.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.6
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• pp.460-465
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• 2016

Long pipe structures are usually installed in fixtures located with regular intervals or laid underground. Therefore, deflection and deformation could easily occur due to their weight or ground activity. A shape monitoring technique can be used effectively to evaluate the integrity of the pipe structures. Fiber Bragg grating (FBG) sensors, which have an advantage of multiplexing could be used to measure strains at multiple-points of a long structure. In this study, to evaluate the integrity of a pipeline, a shape estimation technique based on strain information was proposed. Furthermore, different experiments were conducted to verify the performance of the proposed technique. Thus, the proposed shape estimation technique can represent the shape according to the deformation of the specimen using the FBGs. Moreover, calculated deflection of the pipeline using the estimation technique showed a good agreement with the actual deflection of the pipeline.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.8
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• pp.619-627
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• 2017

MIMO(Multi-Input Multi-Output) radar is an emerging radar technology for its numerous advantages. However, in the electric warfare viewpoint, MIMO radar is a new developed radar technology for that existing parameter estimation cannot applied and a new radar parameter estimation based on the characteristics of MIMO radar is desired. In this paper, we propose a blind estimation scheme for the number of orthogonal waveforms of a uniform linear array(ULA) MIMO radar using minimum two electronic sensors.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7941-7948
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• 2015

Rolling noises during train operation are caused by vibration excited from irregularities of surface roughness between wheel and rail. Therefore, a proper measurement and analysis techniques of acoustic roughness between wheel and rail surface are required for transmission, prediction, and analysis of the train rolling noise. However, since current measuring devices and methods use trolley-based manual handling devices, the measurements induce unstable measuring speed and vibrational interface that increases errors and disturbances. In this paper, a new automatic rail surface exploring platform with a speed controller has been developed for improving measurement accuracy and reducing inconsistency of measurements. In addition, we propose a data integration method of the rail surface roughness with multiple homogeneous displacement sensors and verified the accuracy of the integrated data through standard test-bed railway track investigation.

• Journal of Sensor Science and Technology
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• v.29 no.6
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• pp.407-411
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• 2020

In this study, we propose an array-type gas sensor with high selectivity and response using multiple oxide semiconductors. The sensor array was composed of SnO₂ and In₂O₃, and the detection characteristics were improved by using Pt, Au, and Pd catalysts. All samples were deposited directly on the Pt interdigitated electrode (IDE) through the e-beam evaporator glancing angle deposition (GAD) method. They grew in the form of well-aligned nanorods at off-axis angles. The prepared SnO₂ and In₂O₃ nanorod samples were exposed to CH₃COCH₃, C₇H₈, and NO₂ gases in a 300℃ dry condition. Au-decorated SnO₂, Au-decorated In₂O₃, and Pd-decorated In₂O₃ exhibited high selectivity for CH₃COCH₃, C₇H₈, and NO₂, respectively. They demonstrated a high detection limit of the sub ppb level computationally. In addition, measurements from each sensor were executed in the 40% relative humidity condition. Although there was a slight reduction in detection response, high selectivity and distinguishable detection characteristics were confirmed.

• Journal of KIISE
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• v.43 no.7
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• pp.763-772
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• 2016

With the spread of smartphones containing multiple on-board sensors, the market for context aware applications have grown. However, due to the limited power capacity of a smartphone, users feel discontented QoS. Additionally, context aware applications require the utilization of many forms of context and sensing information. If context transition has occurred, types of needed sensors must be changed and each sensor modules need to turn on/off. In addition, excessive sensing has been found when the context decision is ambiguous. In this paper, we focus on power consumption associated with the context transition that occurs during indoor/outdoor detection, modeling the activities of the sensor associated with these contexts. And we suggest a freezing algorithm that reduces power consumption in context transition. We experiment with a commercial application that service is indoor/outdoor location tracking, measure power consumption in context transition with and without the utilization of the proposed method. We find that proposed method reduces power consumption about 20% during context transition.

• Journal of the Korean Association of Geographic Information Studies
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• v.12 no.4
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• pp.113-124
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• 2009

A research of grid computing that is the combination of computer resources from multiple distributed administrative domains to get large amount of computing power is underway. This research is an application of grid technology on a urban facilities management system based on u-GIS. The sensors are set up in the urban facilities to make it monitoring. If an amount of sensors and gateways is increased, the server needs more computing resources to process the data. In this study we developed the skills that can distribute jobs to idle gateway, in case of the server capacity had approached threshold. It will be possible to develop of economic and efficient system that will apply to large amount of data processing about u-City.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.329-339
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• 2003

In this study, an approach of image fusion in decision level has been proposed for unsupervised image classification using the images acquired from multiple sensors with different characteristics. The proposed method applies separately for each sensor the unsupervised image classification scheme based on spatial region growing segmentation, which makes use of hierarchical clustering, and computes iteratively the maximum likelihood estimates of fuzzy class vectors for the segmented regions by EM(expected maximization) algorithm. The fuzzy class vector is considered as an indicator vector whose elements represent the probabilities that the region belongs to the classes existed. Then, it combines the classification results of each sensor using the fuzzy class vectors. This approach does not require such a high precision in spatial coregistration between the images of different sensors as the image fusion scheme of pixel level does. In this study, the proposed method has been applied to multispectral SPOT and AIRSAR data observed over north-eastern area of Jeollabuk-do, and the experimental results show that it provides more correct information for the classification than the scheme using an augmented vector technique, which is the most conventional approach of image fusion in pixel level.

• Journal of Information Processing Systems
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• v.14 no.6
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• pp.1445-1456
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• 2018

Environment perception and three-dimensional (3D) reconstruction tasks are used to provide unmanned ground vehicle (UGV) with driving awareness interfaces. The speed of obstacle segmentation and surrounding terrain reconstruction crucially influences decision making in UGVs. To increase the processing speed of environment information analysis, we develop a CPU-GPU hybrid system of automatic environment perception and 3D terrain reconstruction based on the integration of multiple sensors. The system consists of three functional modules, namely, multi-sensor data collection and pre-processing, environment perception, and 3D reconstruction. To integrate individual datasets collected from different sensors, the pre-processing function registers the sensed LiDAR (light detection and ranging) point clouds, video sequences, and motion information into a global terrain model after filtering redundant and noise data according to the redundancy removal principle. In the environment perception module, the registered discrete points are clustered into ground surface and individual objects by using a ground segmentation method and a connected component labeling algorithm. The estimated ground surface and non-ground objects indicate the terrain to be traversed and obstacles in the environment, thus creating driving awareness. The 3D reconstruction module calibrates the projection matrix between the mounted LiDAR and cameras to map the local point clouds onto the captured video images. Texture meshes and color particle models are used to reconstruct the ground surface and objects of the 3D terrain model, respectively. To accelerate the proposed system, we apply the GPU parallel computation method to implement the applied computer graphics and image processing algorithms in parallel.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.