• 한국안전학회지
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• 제27권3호
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• pp.20-27
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• 2012

Mechanical seals are one of main components of high speed centrifugal pumps. So, it is very important to detect the faults (scratch, notch, indentation, wear) of mechanical seals since the damage of seal can cause a critical failures or accidents of machinery system. In the past, many researchers mainly performed to detect the seal fault using the time signals measured from sensors. Recently, studies are focused on the development of on-line real time monitoring system. But study on the feature parameters used for fault detection of mechanical seals has a little been performed. In this paper, we showed feature parameters extracted from accelerated and acoustic signals by using the discrete wavelet transform (DWT), alpha coefficient, statistical parameters. And also verified the possibility for fault detection of mechanical seal.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권9호
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• pp.3177-3195
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• 2014

In many moving object detection problems of an aerial video, accurate and robust stabilization is of critical importance. In this paper, a novel accurate image alignment algorithm for aerial electronic image stabilization (EIS) is described. The feature points are first selected using optimal derivative filters based Harris detector, which can improve differentiation accuracy and obtain the precise coordinates of feature points. Then we choose the Delaunay Triangulation edges to find the matching pairs between feature points in overlapping images. The most "useful" matching points that belong to the background are used to find the global transformation parameters using the projective invariant. Finally, intentional motion of the camera is accumulated for correction by Sage-Husa adaptive filtering. Experiment results illustrate that the proposed algorithm is applied to the aerial captured video sequences with various dynamic scenes for performance demonstrations.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권12호
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• pp.4835-4848
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• 2020

The recent expansion of the digital content market is increasing the technical demand for various facial image transformations within the virtual environment. The recent image translation technology enables changes between various domains. However, current image-to-image translation techniques do not provide stable performance through unsupervised learning, especially for shape learning in the face transition field. This is because the face is a highly sensitive feature, and the quality of the resulting image is significantly affected, especially if the transitions in the eyes, nose, and mouth are not effectively performed. We herein propose a new unsupervised method that can transform an in-wild face image into another face style through radical transformation. Specifically, the proposed method applies two face-specific feature loss functions for a generative adversarial network. The proposed technique shows that stable domain conversion to other domains is possible while maintaining the image characteristics in the eyes, nose, and mouth.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권4호
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• pp.2042-2059
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• 2019

Digital shadow puppet has traditionally relied on expensive motion capture equipments and complex design. In this paper, a low-cost driven technique is presented, that captures human pose estimation data with simple camera from real scenarios, and use them to drive virtual Chinese shadow play in a 2.5D scene. We propose a special method for extracting human pose data for driving virtual Chinese shadow play, which is called 2.5D human pose estimation. Firstly, we use the 3D human pose estimation method to obtain the initial data. In the process of the following transformation, we treat the depth feature as an implicit feature, and map body joints to the range of constraints. We call the obtain pose data as 2.5D pose data. However, the 2.5D pose data can not better control the shadow puppet directly, due to the difference in motion pattern and composition structure between real pose and shadow puppet. To this end, the 2.5D pose data transformation is carried out in the implicit pose mapping space based on self-network and the final 2.5D pose expression data is produced for animating shadow puppets. Experimental results have demonstrated the effectiveness of our new method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제17권10호
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• pp.2788-2808
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• 2023

Brain tumors are one of the most threatening malignancies for humans. Misdiagnosis of brain tumors can result in false medical intervention, which ultimately reduces a patient's chance of survival. Manual identification and segmentation of brain tumors from Magnetic Resonance Imaging (MRI) scans can be difficult and error-prone because of the great range of tumor tissues that exist in various individuals and the similarity of normal tissues. To overcome this limitation, the Amended Convolutional Neural Network (ACNN) model has been introduced, a unique combination of three techniques that have not been previously explored for brain tumor detection. The three techniques integrated into the ACNN model are image tissue preprocessing using the Kalman Bucy Smoothing Filter to remove noisy pixels from the input, image tissue segmentation using the Isotonic Regressive Image Tissue Segmentation Process, and feature extraction using the Marr Wavelet Transformation. The extracted features are compared with the testing features using a sigmoid activation function in the output layer. The experimental findings show that the suggested model outperforms existing techniques concerning accuracy, precision, sensitivity, dice score, Jaccard index, specificity, Positive Predictive Value, Hausdorff distance, recall, and F1 score. The proposed ACNN model achieved a maximum accuracy of 98.8%, which is higher than other existing models, according to the experimental results.

• 대한의용생체공학회:의공학회지
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• 제20권4호
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• pp.435-442
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• 1999

본 연구에서는 PVC를 분류하기 위하여 웨이브렛 계수를 기반으로 하는 fuzzy-ART 네트워크를 설계하였다. 설계된 네트워크는 feature를 추출하는 부분과 fuzzy-ART 네트워크를 학습시키는 부분으로 구성된다. 우선 feature의 문턱치 구간을 설정하기 위하여 심전도 신호의 QRS를 검출하였고, 검출된 QRS는 Haar 웨이브렛을 이용한 웨이브렛 변환에 의해 주파수 분할하였다. 분할된 주파수 중에서 입력 feature를 추출하기 위하여 저주파 영역의 6번째 계수(D6)만을 선택하였다. D6신호는 입력 feature를 구성하기 위한 문턱치를 적용하여 fuzzy-ART 네트워크의 2진수 입력 feature로 전환하였고, PVC를 분류하기 위하여 fuzzy-ART네트워크를 학습시켰다. 본 연구의 성능을 평가하기 위하여 PVC가 포함된 MIT/BIH 데이터 베이스가 사용되었으며, fuzzy-ART 네트워크의 분류성능은 96.25%이었다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.2393-2398
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• 2003

In this paper, we proposes a new fuzzy inference system for modeling nonlinear systems given input and output data. In the suggested fuzzy inference system, the number of fuzzy rules and parameter values of membership functions are automatically decided by using the kernel-based method. The kernel-based method individually performs linear transformation and kernel mapping. Linear transformation projects input space into linearly transformed input space. Kernel mapping projects linearly transformed input space into high dimensional feature space. The structure of the proposed fuzzy inference system is equal to a Takagi-Sugeno fuzzy model whose input variables are weighted linear combinations of input variables. In addition, the number of fuzzy rules can be reduced under the condition of optimizing a given criterion by adjusting linear transformation matrix and parameter values of kernel functions using the gradient descent method. Once a structure is selected, coefficients in consequent part are determined by the least square method. Simulated result illustrates the effectiveness of the proposed technique.

• Journal of Power Electronics
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• 제8권1호
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• pp.91-100
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• 2008

The d-q harmonic detecting algorithms are dominant methods to generate current references for active power filters (APF). They are often implemented in the synchronous frame and time domain. This paper researches the frequency characteristics of d-q synchronous transformations, which are closely related to the analysis and design issues of control system. Intuitively, the synchronous transformation is explained with amplitude modulation (AM) in this paper. Then, the synchronous filter is proven to be a time-invariant and linear system, and its transfer function matrix is derived in the stationary frames. These frequency-domain models imply that the synchronous transformation has an equivalent effect of frequency transformation. It is because of this feature, the d-q method achieves band-pass characteristics with the low pass filters in the synchronous frame at run time. To simplify these analytical models, an instantaneous positive-negative sequence frame is proposed as expansion of traditional symmetrical components theory. Furthermore, the synchronous filter is compared with the traditional bind-pass filters based on these frequency-domain analytical models. The d-q harmonic detection methods are also improved to eliminate the inherent coupling effect of synchronous transformation. Typical examples are given to verify previous analysis and comparison. Simulation and experimental results are also provided for verification.

• 로봇학회논문지
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• 제4권3호
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• pp.185-191
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• 2009

This paper presents a new shape-based algorithm using affine category shape model for object category recognition and model learning. Affine category shape model is a graph of interconnected nodes whose geometric interactions are modeled using pairwise potentials. In its learning phase, it can efficiently handle large pose variations of objects in training images by estimating 2-D homography transformation between the model and the training images. Since the pairwise potentials are defined on only relative geometric relationship betweenfeatures, the proposed matching algorithm is translation and in-plane rotation invariant and robust to affine transformation. We apply spectral matching algorithm to find feature correspondences, which are then used as initial correspondences for RANSAC algorithm. The 2-D homography transformation and the inlier correspondences which are consistent with this estimate can be efficiently estimated through RANSAC, and new correspondences also can be detected by using the estimated 2-D homography transformation. Experimental results on object category database show that the proposed algorithm is robust to pose variation of objects and provides good recognition performance.

• 대한전기학회논문지:전력기술부문A
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• 제51권8호
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• pp.409-416
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• 2002

This paper presents a fault location algorithm using direct 3-phase circuit analysis for distribution power networks. The unbalanced feature of distribution networks due to single phase loads or asymmetric operation prohibits us from using the conventional symmetrical component transformation. Even though the symmetrical component transformation provides us with a very easy tool in three phase network analysis, it is limited to balanced systems in utilizing its strong point, which is not suitable for distribution networks. In this paper, a fault location algorithm using direct 3-phase circuit analysis is developed. The algorithm is derived and it Is shown that the proposed method if we use matrix inverse lemma, is not more difficult then the conventional methods using symmetrical component transformation. Since the symmetrical component transformation is not used in the suggested method, unbalanced networks also can be handled with the same difficulty as balanced networks. The case study results show the correctness and effectiveness of the proposed algorithm.