• Structural Monitoring and Maintenance
• /
• v.5 no.1
• /
• pp.51-65
• /
• 2018

A number of sensing techniques have been implemented for detecting defects in civil infrastructures instead of onsite human inspections in structural health monitoring. However, the issue of faults in sensors has not received much attention. This issue may lead to incorrect interpretation of data and false alarms. To overcome these challenges, this article presents a deep learning-based method with a new architecture of Stateful Long Short Term Memory Neural Networks (S-LSTM NN) for detecting sensor fault without going into details of the fault features. As LSTMs are capable of learning data features automatically, and the proposed method works without an accurate mathematical model. The detection of four types of sensor faults are studied in this paper. Non-stationary acceleration responses of a three-span continuous bridge when under operational conditions are studied. A deep network model is applied to the measured bridge data with estimation to detect the sensor fault. Another set of sensor output data is used to supervise the network parameters and backpropagation algorithm to fine tune the parameters to establish a deep self-coding network model. The response residuals between the true value and the predicted value of the deep S-LSTM network was statistically analyzed to determine the fault threshold of sensor. Experimental study with a cable-stayed bridge further indicated that the proposed method is robust in the detection of the sensor fault.

• Proceedings of the KIPE Conference
• /
• 2004.07a
• /
• pp.274-278
• /
• 2004

This paper presents a strategy to drive built in-type spindle induction motor which is used as CNC (Computer Numerical Control) in the industrial world. The direct vector control which is robust to the changed machine parameters in the high speed range is used in this motor control method. And electrical model of induction motor presents the basic idea based on observer structure, which is composed of voltage model and current model. But the former has the defects in low speed range, the latter has the defects of sensitivity to motor parameter. Thus Gopinath model flux estimator which is the closed loop flux observer based on two models for the rotor flut estimation is used in this paper. Moreover this paper presents to drive the spindle motor in the high speed range by using the flux weakening control.

• Computers and Concrete
• /
• v.8 no.1
• /
• pp.71-96
• /
• 2011

Existing reinforced concrete (RC) beams can be strengthened with externally bolted steel plates to the sides of beams. The effectiveness of this type of bolted side-plate (BSP) beam can however be affected by partial interaction between the steel plates and RC beams due to the mechanical slip of bolts. To avoid over-estimation of the flexural strength and ensure accurate prediction of the load-deformation response of the beams, the effect of partial interaction has to be properly considered. In this paper, a special non-linear macro-finite-element model that takes into account the effects of partial interaction is proposed. The RC beam and the steel plates are modelled as two different elements, interacting through discrete groups of bolts. A layered method is adopted for the formulation of the RC beam and steel plate elements, while a special non-linear model based on a kinematic hardening assumption for the bolts is used to simulate the bolt group effect. The computer program SiBAN was developed based on the proposed approach. Comparison with the available experimental results shows that SiBAN can accurately predict the partial interaction behaviour of the BSP beams. Further numerical simulations show that the interaction between the RC beam and the steel plates is greatly reduced by the formation of plastic hinges and should be considered in analyses of the strengthened beams.

• Journal of Computational Design and Engineering
• /
• v.2 no.1
• /
• pp.67-72
• /
• 2015

Recently, along with the improvements of geometry modeling methods using sketch-based interface, there have been a lot of developments in research about generating surface model from 3D curves. However, surfacing a 3D curve network remains an ambiguous problem due to the lack of geometric information. In this paper, we propose a new algorithm for estimating the normal vectors of the 3D curves which accord closely with user intent. Bending energy is defined by utilizing RMF(Rotation-Minimizing Frame) of 3D curve, and we estimated this minimal energy frame as the one that accords design intent. The proposed algorithm is demonstrated with surface model creation of various curve networks. The algorithm of estimating geometric information in 3D curves which is proposed in this paper can be utilized to extract new information in the sketch-based modeling process. Also, a new framework of 3D modeling can be expected through the fusion between curve network and surface creating algorithm.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.7
• /
• pp.3239-3267
• /
• 2018

Image and video dehazing is a popular topic in the field of computer vision and digital image processing. A fast, optimized dehazing algorithm was recently proposed that enhances contrast and reduces flickering artifacts in a dehazed video sequence by minimizing a cost function that makes transmission values spatially and temporally coherent. However, its fixed-size block partitioning leads to block effects. The temporal cost function also suffers from the temporal non-coherence of newly appearing objects in a scene. Further, the weak edges in a hazy image are not addressed. Hence, a video dehazing algorithm based on well designed spectral clustering is proposed. To avoid block artifacts, the spectral clustering is customized to segment static scenes to ensure the same target has the same transmission value. Assuming that edge images dehazed with optimized transmission values have richer detail than before restoration, an edge intensity function is added to the spatial consistency cost model. Atmospheric light is estimated using a modified quadtree search. Different temporal transmission models are established for newly appearing objects, static backgrounds, and moving objects. The experimental results demonstrate that the new method provides higher dehazing quality and lower time complexity than the previous technique.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.9 no.8
• /
• pp.2928-2947
• /
• 2015

Adaptive Gaussian filter (AGF) is a recently developed switching filter to remove salt and pepper noise. AGF first directly identifies pixels of gray levels 0 and 255 as noise pixels, and then only restored noise pixels using a Gaussian filter with adaptive variance based on the estimated noise density. AGF usually achieves better denoising effect in comparison with other filters. However, AGF still fails to obtain good denoising effect on images with noise-free pixels of gray levels 0 and 255, due to its severe false alarm in its noise detection stage. To alleviate this issue, a modified version of AGF is proposed in this paper. Specifically, the proposed filter first performs noise detection via an image block based noise density estimation and sequential noise density guided rectification on the noise detection result of AGF. Then, a modified Gaussian filter with adaptive variance and window size is used to restore the detected noise pixels. The proposed filter has been extensively evaluated on two representative grayscale images and the Berkeley image dataset BSDS300 with 300 images. Experimental results showed that the proposed filter achieved better denoising effect over the state-of-the-art filters, especially on images with noise-free pixels of gray levels 0 and 255.

• 한국정보컨버전스학회:학술대회논문집
• /
• 2008.06a
• /
• pp.145-150
• /
• 2008

This thesis explores the problem of reconstructing a three-dimensional(3D) scene given a set of images or image sequences of the scene. It describes efficient methods for the 3D reconstruction of static and dynamic scenes from stereo images, stereo image sequences, and images captured from multiple viewpoints. Novel methods for image-based and volumetric modelling approaches to 3D reconstruction are presented, with an emphasis on the development of efficient algorithm which produce high quality and accurate reconstructions. For image-based 3D reconstruction a novel energy minimisation scheme, Iterated Dynamic Programming, is presented for the efficient computation of strong local minima of discontinuity preserving energyy functions. Coupled with a novel morphological decomposition method and subregioning schemes for the efficient computation of a narrowband matching cost volume. the minimisation framework is applied to solve problems in stereo matching, stereo-temporal reconstruction, motion estimation, 2D image registration and 3D image registration. This thesis establishes Iterated Dynamic Programming as an efficient and effective energy minimisation scheme suitable for computer vision problems which involve finding correspondences across images. For 3D reconstruction from multiple view images with arbitrary camera placement, a novel volumetric modelling technique, Embedded Voxel Colouring, is presented that efficiently embeds all reconstructions of a 3D scene into a single output in a single scan of the volumetric space under exact visibility. An adaptive thresholding framework is also introduced for the computation of the optimal set of thresholds to obtain high quality 3D reconstructions. This thesis establishes the Embedded Voxel Colouring framework as a fast, efficient and effective method for 3D reconstruction from multiple view images.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2001.04a
• /
• pp.102-109
• /
• 2001

In order to take account of the statistical properties of probability variables used in the structural analysis, the conventional approach using the safety factor based on past experience usually estimated the safety of a structure. The real structures could only be analyzed with the error in estimation of loads, material characters and the dimensions of the members. But the errors should be considered systematically in the structural analysis. Structural safety could not precisely be appraised by the traditional structural design concept. Recently, new approach based on the probability concept has been applied to the assessment of structural safety using the reliability concept. Thus, the computer program by the Probabilistic FEM is developed by incorporating the probabilistic concept into the conventional FEM method. This paper estimated for the reliability of a plane stress structure by Advanced First-Order Second Moment method using von Mises, Tresca and Mohr-Coulomb failure criterions. The reliability index and failure probability of attained by the Monte Carlo Simulation method with the von Mises criterion were same as PFEM, but the Monte Carlo Simulation were very time-consuming. The variance of member thickness and load could influence the reliability and failure probability most sensitively among the design variables from the results of the parameter analysis. And proper failure criterion must be used to design safely.

• Journal of Institute of Control, Robotics and Systems
• /
• v.21 no.8
• /
• pp.766-772
• /
• 2015

This paper proposes an object detection method based on motion estimation using background subtraction in the fisheye images obtained through omni-directional camera mounted on the vehicle. Recently, most of the vehicles installed with rear camera as a standard option, as well as various camera systems for safety. However, differently from the conventional object detection using the image obtained from the camera, the embedded system installed in the vehicle is difficult to apply a complicated algorithm because of its inherent low processing performance. In general, the embedded system needs system-dependent algorithm because it has lower processing performance than the computer. In this paper, the location of object is estimated from the information of object's motion obtained by applying a background subtraction method which compares the previous frames with the current ones. The real-time detection performance of the proposed method for object detection is verified experimentally on embedded board by comparing the proposed algorithm with the object detection based on LKOF (Lucas-Kanade optical flow).

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.119-124
• /
• 2015

As Location-Based Services (LBSs) of smartphones increases, the power consumption of a smartphone due to Global Positioning System (GPS) is becoming increasingly serious. This paper presents a new location estimation scheme for smartphones called Adaptive Location Detection (ALD). ALD adaptively detects the location of a smartphone considering the movement pattern of a user, category of applications executed in the smartphone, and the battery level. Simulation with various real applications and scenarios show that ALD reduces 37% of energy consumption compared to GPS. Nevertheless, it satisfies the accuracy requirement of each situation.