• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.6
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• pp.563-569
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• 2009

In this paper, four different types of pulsed eddy current(PEC) probe are designed and their performance of detecting wall thickness reduction is compared. By using the backward difference method in time and the finite element method in space, PEC signals from various thickness and materials are numerically calculated and three features of the signal are selected. Since PEC signals and features are obtained by various types and sizes of probe, the comparison is made through the normalized features which reflect the sensitivity of the feature to thickness reduction. The normalized features indicate that the shielded reflection probe provides the best sensitivity to wall thickness reduction for all three signal features. Results show that the best sensitivity to thickness reduction can be achieved by the peak value, but also suggest that the time to peak can be a good candidate because of its linear relationship with the thickness variation.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.230-240
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• 2022

Sharing of online videos via internet is an emerging and important concept in different types of applications like surveillance and video mobile search in different web related applications. So there is need to manage personalized web video retrieval system necessary to explore relevant videos and it helps to peoples who are searching for efficient video relates to specific big data content. To evaluate this process, attributes/features with reduction of dimensionality are computed from videos to explore discriminative aspects of scene in video based on shape, histogram, and texture, annotation of object, co-ordination, color and contour data. Dimensionality reduction is mainly depends on extraction of feature and selection of feature in multi labeled data retrieval from multimedia related data. Many of the researchers are implemented different techniques/approaches to reduce dimensionality based on visual features of video data. But all the techniques have disadvantages and advantages in reduction of dimensionality with advanced features in video retrieval. In this research, we present a Novel Intent based Dimension Reduction Semi-Supervised Learning Approach (NIDRSLA) that examine the reduction of dimensionality with explore exact and fast video retrieval based on different visual features. For dimensionality reduction, NIDRSLA learns the matrix of projection by increasing the dependence between enlarged data and projected space features. Proposed approach also addressed the aforementioned issue (i.e. Segmentation of video with frame selection using low level features and high level features) with efficient object annotation for video representation. Experiments performed on synthetic data set, it demonstrate the efficiency of proposed approach with traditional state-of-the-art video retrieval methodologies.

• Journal of Korea Multimedia Society
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• v.16 no.7
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• pp.820-828
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• 2013

Extracting unique features from an image is a fundamental issue when making panorama images, acquiring stereo images, recognizing objects and analyzing images. Generally, the task to compare features to other images requires much computing time because some features are formed as a vector which has many elements. In this paper, we present a method that compares features after reducing the feature dimension extracted from an image using PCA(principal component analysis) and sorting the features in a linked list. SURF(speeded up robust features) is used to describe image features. When the dimension reduction method is applied, we can reduce the computing time without decreasing the matching accuracy. The proposed method is proved to be fast and robust in experiments.

• Journal of Welding and Joining
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• v.28 no.2
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• pp.74-78
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• 2010

In this paper, a non-contact method of evaluating the thickness reduction in an aluminum sheet caused by corrosion and friction using SH-EMAT (shear horizontal, electromagnetic acoustic transducer) is described. Since this method is based on the measurement of the time-of-flight and amplitude change of guided waves caused from the thickness reduction, it provides information on the thinning defects. Information was obtained on the changes of the various wave features, such as their time-of-flight and amplitude, and their correlations with the thickness reduction were investigated. The interesting features in the dispersive behavior of selected guided modes were used for the detection of thinning defects. The measurements of these features using SH waves were performed on aluminum specimens with regions thinned by 7.2% to 29.5% of the total thickness. It is shown that the time-of-flight measurement provides an estimation of the thickness reduction and length of the thinning defects.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.468-488
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• 2016

The paper proposes a novel framework for 3D face verification using dimensionality reduction based on highly distinctive local features in the presence of illumination and expression variations. The histograms of efficient local descriptors are used to represent distinctively the facial images. For this purpose, different local descriptors are evaluated, Local Binary Patterns (LBP), Three-Patch Local Binary Patterns (TPLBP), Four-Patch Local Binary Patterns (FPLBP), Binarized Statistical Image Features (BSIF) and Local Phase Quantization (LPQ). Furthermore, experiments on the combinations of the four local descriptors at feature level using simply histograms concatenation are provided. The performance of the proposed approach is evaluated with different dimensionality reduction algorithms: Principal Component Analysis (PCA), Orthogonal Locality Preserving Projection (OLPP) and the combined PCA+EFM (Enhanced Fisher linear discriminate Model). Finally, multi-class Support Vector Machine (SVM) is used as a classifier to carry out the verification between imposters and customers. The proposed method has been tested on CASIA-3D face database and the experimental results show that our method achieves a high verification performance.

• Journal of Information Processing Systems
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• v.14 no.5
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• pp.1167-1175
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• 2018

Microarray data plays an essential role in diagnosing and detecting cancer. Microarray analysis allows the examination of levels of gene expression in specific cell samples, where thousands of genes can be analyzed simultaneously. However, microarray data have very little sample data and high data dimensionality. Therefore, to classify microarray data, a dimensional reduction process is required. Dimensional reduction can eliminate redundancy of data; thus, features used in classification are features that only have a high correlation with their class. There are two types of dimensional reduction, namely feature selection and feature extraction. In this paper, we used k-means algorithm as the clustering approach for feature selection. The proposed approach can be used to categorize features that have the same characteristics in one cluster, so that redundancy in microarray data is removed. The result of clustering is ranked using the Relief algorithm such that the best scoring element for each cluster is obtained. All best elements of each cluster are selected and used as features in the classification process. Next, the Random Forest algorithm is used. Based on the simulation, the accuracy of the proposed approach for each dataset, namely Colon, Lung Cancer, and Prostate Tumor, achieved 85.87%, 98.9%, and 89% accuracy, respectively. The accuracy of the proposed approach is therefore higher than the approach using Random Forest without clustering.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.2
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• pp.832-854
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• 2019

Action recognition has been studied in computer vision field for years. We present an effective approach to recognize actions using a dimension reduction method, which is applied as a crucial step to reduce the dimensionality of feature descriptors after extracting features. We propose to use sparse matrix and randomized kd-tree to modify it and then propose modified Local Fisher Discriminant Analysis (mLFDA) method which greatly reduces the required memory and accelerate the standard Local Fisher Discriminant Analysis. For feature encoding, we propose a useful encoding method called mix encoding which combines Fisher vector encoding and locality-constrained linear coding to get the final video representations. In order to add more meaningful features to the process of action recognition, the convolutional neural network is utilized and combined with mix encoding to produce the deep network feature. Experimental results show that our algorithm is a competitive method on KTH dataset, HMDB51 dataset and UCF101 dataset when combining all these methods.

• Proceedings of the KSRS Conference
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• 2003.11a
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• pp.700-702
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• 2003

A new method for automatic detection of absorption features is proposed. This method is based on the modulus maximum of the scale-space image calculated by continuous wavelet transform. This method is computationally efficient as compared to traditional methods. The continuum removal algorithm is than implemented on the detected absorption features to reduce some additive factors caused by other absorbing of materials. The results show that the chlorophyll absorption features are detected exactly.

• Advances in pediatric surgery
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• v.16 no.2
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• pp.108-116
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• 2010

Air reduction is a safe, effective, and fast initial treatment for pediatric intussusception. There is low dose radiation exposure. Factors affecting outcomes of air reduction were analyzed by reviewing the clinical features and results of treatment. A total of 399 out of 485 patients with pediatric intussusceptions were treated at the Seoul National University Children's Hospital from 1996 to 2009. All of the patients received air reduction as the first line of treatment. Clinical features such as gender, age, seasonal variation, symptoms, signs, types, pathologic leading point, and treatment results including success rate, complication, recurrence, NPO time, and duration of hospitalization were reviewed. The Pearson chi-square, student T-, and logistic regression tests were used for statistical analysis. P-value less than 0.05 was considered to be statistically significant. The prevalent clinical features were: male (65.4 %), under one-year of age (40.3 %), ileocolic type (71.9 %), abdominal pain (85.4 %), and accompanying mesentery lymph node enlargement (2.2 %). The overall success rate for air reduction was 78.4 % (313 of 399 patients), and the perforation rate during reduction was 1.5 %. There were 23 recurrent cases over 21.6 months. All were successfully treated with re-do air reduction. Reduction failures had longer overall NPO times (27.067hrs vs. 43.0588hrs; p=0.000) and hospitalization durations (1.738d vs. 6.975d; p=0.000) compared to the successful cases. The factors affecting success rates were fever (p=0.002), abdominal distension (p=0.000), lethargy (p=0.000) and symptom duration (p=0.000) on univariate analysis. Failure rates were higher in patients with symptom durations greater than 24 hours (p=0.023), and lethargy (p=0.003) on multivariate analysis. Air reduction showed high success rates and excellent treatment outcomes as the initial treatment for pediatric intussusception in this study. Symptom duration and lethargy were significantly associated with reduced success rates.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.05a
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• pp.474-475
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• 2013

In healthcare systems, the accuracy of a classifier for classifying medical diseases depends on a reduced dataset. Key to achieve true classification results is the reduction of data to a set of optimal number of significant features. The initial step towards data reduction is the integration of heterogeneous data sources to a unified reduced dataset which is further reduced by considering the range of values of all the attributes and then finally filtering and dropping out the least significant features from the dataset. This paper proposes a three step data reduction model which plays a vital role in the classification process.