• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.5
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• pp.1610-1629
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• 2021

Failures frequently occurred in manufacturing machines due to complex and changeable manufacturing environments, increasing the downtime and maintenance costs. This manuscript develops a novel deep learning-based method named Multi-Domain Convolutional Neural Network (MDCNN) to deal with this challenging task with vibration signals. The proposed MDCNN consists of time-domain, frequency-domain, and statistical-domain feature channels. The Time-domain channel is to model the hidden patterns of signals in the time domain. The frequency-domain channel uses Discrete Wavelet Transformation (DWT) to obtain the rich feature representations of signals in the frequency domain. The statistic-domain channel contains six statistical variables, which is to reflect the signals' macro statistical-domain features, respectively. Firstly, in the proposed MDCNN, time-domain and frequency-domain channels are processed by CNN individually with various filters. Secondly, the CNN extracted features from time, and frequency domains are merged as time-frequency features. Lastly, time-frequency domain features are fused with six statistical variables as the comprehensive features for identifying the fault. Thereby, the proposed method could make full use of those three domain-features for fault diagnosis while keeping high distinguishability due to CNN's utilization. The authors designed massive experiments with 10-folder cross-validation technology to validate the proposed method's effectiveness on the CWRU bearing data set. The experimental results are calculated by ten-time averaged accuracy. They have confirmed that the proposed MDCNN could intelligently, accurately, and timely detect the fault under the complex manufacturing environments, whose accuracy is nearly 100%.

• KIPS Transactions on Software and Data Engineering
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• v.13 no.1
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• pp.17-27
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• 2024

Video frame interpolation is an important technique used in the field of video and media, as it increases the continuity of motion and enables smooth playback of videos. In the study of video frame interpolation using deep learning, Kernel Based Method captures local changes well, but has limitations in handling global changes. In this paper, we propose a new U-Net structure that applies feature map differentiation and two directions to focus on capturing major changes to generate intermediate frames more accurately while reducing the number of parameters. Experimental results show that the proposed structure outperforms the existing model by up to 0.3 in PSNR with about 61% fewer parameters on common datasets such as Vimeo, Middle-burry, and a new YouTube dataset. Code is available at https://github.com/Go-MinSeong/SF-AdaCoF.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1309-1320
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• 2023

The technological development and popularization of mobile devices have developed so that users can check their location anywhere and use the Internet. However, in the case of indoors, the Internet can be used smoothly, but the global positioning system (GPS) function is difficult to use. There is an increasing need to provide real-time location information in shaded areas where GPS is not received, such as department stores, museums, conference halls, schools, and tunnels, which are indoor public places. Accordingly, research on the recent indoor positioning technology based on light detection and ranging (LiDAR) equipment is increasing to build a landmark database. Focusing on the accessibility of building a landmark database, this study attempted to develop a technique for estimating the user's location by using a single image taken of a landmark based on a mobile device and the landmark database information constructed in advance. First, a landmark database was constructed. In order to estimate the user's location only with the mobile image photographing the landmark, it is essential to detect the landmark from the mobile image, and to acquire the ground coordinates of the points with fixed characteristics from the detected landmark. In the second step, by applying the bag of words (BoW) image search technology, the landmark photographed by the mobile image among the landmark database was searched up to a similar 4th place. In the third step, one of the four candidate landmarks searched through the scale invariant feature transform (SIFT) feature point extraction technique and Homography random sample consensus(RANSAC) was selected, and at this time, filtering was performed once more based on the number of matching points through threshold setting. In the fourth step, the landmark image was projected onto the mobile image through the Homography matrix between the corresponding landmark and the mobile image to detect the area of the landmark and the corner. Finally, the user's location was estimated through the location estimation technique. As a result of analyzing the performance of the technology, the landmark search performance was measured to be about 86%. As a result of comparing the location estimation result with the user's actual ground coordinate, it was confirmed that it had a horizontal location accuracy of about 0.56 m, and it was confirmed that the user's location could be estimated with a mobile image by constructing a landmark database without separate expensive equipment.

• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.140-146
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• 2011

In this paper, we propose a fast object classification method based on texture and color information for video surveillance. We take the advantage of local patches by extracting SURF and color histogram from images. SURF gives intensity content information and color information strengthens distinctiveness by providing links to patch content. We achieve the advantages of fast computation of SURF as well as color cues of objects. We use Bag of Word models to generate global descriptors of a region of interest (ROI) or an image using the local features, and Na$\ddot{i}$ve Bayes model for classifying the global descriptor. In this paper, we also investigate discriminative descriptor named Scale Invariant Feature Transform (SIFT). Our experiment result for 4 classes of the objects shows 95.75% of classification rate.

• International Journal of Control, Automation, and Systems
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• v.1 no.2
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• pp.194-202
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• 2003

In this paper, we introduce a category of Multi-FNN (Fuzzy-Neural Networks) models, analyze the underlying architectures and propose a comprehensive identification framework. The proposed Multi-FNNs dwell on a concept of fuzzy rule-based FNNs based on HCM clustering and evolutionary fuzzy granulation, and exploit linear inference being treated as a generic inference mechanism. By this nature, this FNN model is geared toward capturing relationships between information granules known as fuzzy sets. The form of the information granules themselves (in particular their distribution and a type of membership function) becomes an important design feature of the FNN model contributing to its structural as well as parametric optimization. The identification environment uses clustering techniques (Hard C - Means, HCM) and exploits genetic optimization as a vehicle of global optimization. The global optimization is augmented by more refined gradient-based learning mechanisms such as standard back-propagation. The HCM algorithm, whose role is to carry out preprocessing of the process data for system modeling, is utilized to determine the structure of Multi-FNNs. The detailed parameters of the Multi-FNN (such as apexes of membership functions, learning rates and momentum coefficients) are adjusted using genetic algorithms. An aggregate performance index with a weighting factor is proposed in order to achieve a sound balance between approximation and generalization (predictive) abilities of the model. To evaluate the performance of the proposed model, two numeric data sets are experimented with. One is the numerical data coming from a description of a certain nonlinear function and the other is NOx emission process data from a gas turbine power plant.

• Journal of IKEEE
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• v.22 no.3
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• pp.590-598
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• 2018

Recently, interest in IoT(Internet of Things) technology, which provides Internet services to objects, is increasing. IoT offers a variety of services in home networks, healthcare, and disaster alerts. IoT with LLN(Low Power & Lossy Networks) feature frequently loses sensor node. RPL, the standard routing protocol of IoT, performs global repair when data loss occurs in a sensor node. However, frequent loss of sensor nodes due to lower sensor nodes causes network performance degradation due to frequent full path reset. In this paper, we propose an additional selection method of the storage mode sensor node to solve the network degradation problem due to the frequent path resetting problem even after selecting the storage mode sensor node, and propose a method of equalizing the total path resetting number of each instance.

• The KIPS Transactions:PartB
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• v.10B no.2
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• pp.135-142
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• 2003

Robust extraction of 3D object's features, shape and global motion information from 2D image sequence is described. The object's 21 feature points on the pyramid type synthetic object are extracted automatically using color transform technique. The extracted features are used to recover the 3D shape and global motion of the object using stereo paraperspective camera model and sequential SVD(Singuiar Value Decomposition) factorization method. An inherent error of depth recovery due to the paraperspective camera model was removed by using the stereo image analysis. A 30 synthetic object with 21 features reflecting various position was designed and tested to show the performance of proposed algorithm by comparing the recovered shape and motion data with the measured values.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.27 no.3
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• pp.193-210
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• 1991

For the deckhouse or superstructure, attention is directed to the reduction of vibration from a human susceptibility point of view. The two basic requirements for obtaining a low vibration level in the accommodation are to ensure that excitation forces from propeller and/or main engine are small and to avoid resonance excitation of the hull and superstructure. In recent years increased attention has been directed towards the problems of vibration and noise in deckhouse, which have caused major problems with regard to the environmental quality in the living quarters for crews. Accordingly, in this paper, the characteristic of the vibration of deckhouse of fishing boat, of which the length/height ratio is also relatively high, are studied systematically with regard to the shape and modelling of deckhouse based on finite element method of 1-dimensional, 2-dimensional and 3-dimensional model. This study is divided into 4-part. 1st part is the global deckhouse vibration, 2nd part is the local deckhouse vibration, 3rd part consists of the estimation for stiffness of foundational support and 4th part is the application to TUNA LONG LINER of 416 ton class. For the global vibration analysis, the severity of the vibration depends on the longitudinal shear and bending stiffness of the deckhouse, on the vertical deckhouse support(fore, aft and sides). However, even if the design is technically sound, vibration problems may arise due to vertical or longitudinal hull girder or afterbody resonances. Author applied the method of this study to the analysis of, deep-sea fishing vessel of G.T. 416 ton class with relatively low height and long deckhouse, and investigated the vibrational characteristic of the fishing vessel with earlier structural feature. According to this investigation, the vibration, response of above vessel was confirmed of which main hull and deckhouse behave as one body. It is at the bottom of vibrational trouble which a accommodation part of the fishing vessel is raised, that is the local vibration for side wall, fore-aft wall and deck plate of deckhouse rather than thief fect of fore-aft vibration of deckhouse for above fishing vessel. and the resonance of main hull, deckhouse and driving system such as the main engine, propeller in exciting source is mainly brought up as the trouble.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.25 no.1
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• pp.81-89
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• 2012

This study propose the vibration-based damage monitoring scheme for steel girder bolt-connection member by using wireless acceleration sensor node. In order to achieve the objective, the following approaches are implemented. Firstly, wireless acceleration sensor node is described on the design of hardware components and embedded operation software. Secondly, the vibration-based damage monitoring scheme of the steel girder bolt-connection member is described. The damage monitoring scheme performed global damage occurrence alarming and damage localization estimation by the acceleration response feature analysis. The global damage alarming is applied to the correlation coefficient of power spectral density. The damage localization estimation is applied to the frequency-based damage detection technique and the mode-shape-based damage detection technique. Finally, the performance of the vibration-based damage monitoring scheme is evaluated for detecting the bolt-connection member damage on a lab-scale steel girder.

• Journal of the Korean Institute of Telematics and Electronics C
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• v.35C no.9
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• pp.81-92
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• 1998

A new hybrid technique using several sub-populations having completely different evolutionary behaviors is proposed to increase the possibility to quickly find the global optimum of continuous optimization problem. It has three sub-populations. Two NPOSA algorithms showing good performance in the problem having a rugged fitness function are applied to two sub-populations and a self-adaptive evolutionary algorithm to the other sub-population. Sub-populations evolve in different manners and the interaction among these sub-populations lead to the global optimum quickly. The efficiency of this technique is verified through benchmark test functions. Finally, the algorithm with three sub-populations has been applied to seek for the optimal camera calibration parameters. After an error function has been defined using measured feature points of a calibration block, it has been shown that the algorithm searches for the camera parameters that minimize the error function.