• Journal of the Korean Society for Nondestructive Testing
• /
• v.21 no.6
• /
• pp.658-662
• /
• 2001

The classification of surface flaw types was performed on the basis of angular dependence of backscattered ultrasound. The copper line adhered on the surface, cower line filled in groove, pure groove and the normal edge were adopted as various surface flaw patterns of glass specimen. A backward longitudinal profile was formed probably by the longitudinal wane scattering at and near 1st critical angle. The wave trains at the peak angles of the backward radiation profiles showed different shapes according to the superposition ratio of scattered and leaky waves. The asymmetry of the backward radiation profile arose due to the scattering effect of flaw. The additive resonance effect of copper line appeared in the left side of the profile. The peak angles of both the longitudinal and radiation profiles were shifted toward small angle by the scattering effect.

• ETRI Journal
• /
• v.42 no.1
• /
• pp.78-89
• /
• 2020

Human activity recognition (HAR) has become effective as a computer vision tool for video surveillance systems. In this paper, a novel biometric system that can detect human activities in 3D space is proposed. In order to implement HAR, joint angles obtained using an RGB-depth sensor are used as features. Because HAR is operated in the time domain, angle information is stored using the sliding kernel method. Haar-wavelet transform (HWT) is applied to preserve the information of the features before reducing the data dimension. Dimension reduction using an averaging algorithm is also applied to decrease the computational cost, which provides faster performance while maintaining high accuracy. Before the classification, a proposed thresholding method with inverse HWT is conducted to extract the final feature set. Finally, the K-nearest neighbor (k-NN) algorithm is used to recognize the activity with respect to the given data. The method compares favorably with the results using other machine learning algorithms.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_1
• /
• pp.999-1009
• /
• 2019

The importance of high-resolution sea ice maps of the Arctic Ocean is increasing due to the possibility of pioneering North Pole Routes and the necessity of precise climate prediction models. In this study,sea ice classification algorithms for two deep learning models were examined using Sentinel-1 A/B SAR data to generate high-resolution sea ice classification maps. Based on current ice charts, three classes (Open Water, First Year Ice, Multi Year Ice) of training data sets were generated by Arctic sea ice and remote sensing experts. Ten sea ice classification algorithms were generated by combing two deep learning models (i.e. Simple CNN and Resnet50) and five cases of input bands including incident angles and thermal noise corrected HV bands. For the ten algorithms, analyses were performed by comparing classification results with ground truth points. A confusion matrix and Cohen's kappa coefficient were produced for the case that showed best result. Furthermore, the classification result with the Maximum Likelihood Classifier that has been traditionally employed to classify sea ice. In conclusion, the Convolutional Neural Network case, which has two convolution layers and two max pooling layers, with HV and incident angle input bands shows classification accuracy of 96.66%, and Cohen's kappa coefficient of 0.9499. All deep learning cases shows better classification accuracy than the classification result of the Maximum Likelihood Classifier.

• Journal of Korean Neurosurgical Society
• /
• v.64 no.1
• /
• pp.69-77
• /
• 2021

Objective : Classification systems for cervical ossification of the posterior longitudinal ligament (OPLL) have traditionally focused on the morphological characteristics of ossification. Although the classification describes many clinical features associated with the shape of the ossification, including the concept of spondylosis seems necessary because of the similarity in age distribution. Methods : Patients diagnosed with OPLL who presented with increase signal intensity (ISI) on magnetic resonance imaging were surgically treated in our department. The patients were divided into two groups (pure versus degenerative) according to the presence of disc degeneration. Results : Of 141 patients enrolled in this study, more than half (61%) were classified into the degenerative group. The pure group showed a profound male predominance, early presentation of myelopathy, and a different predilection for ISI compared to the degenerative group. The mean canal compromise ratio (CC) of the ISI was 47% in the degenerative group versus 61% in the pure group (p<0.0000). On the contrary, the global and segment motions were significantly larger in the degenerative group (p<0.0000 and p=0.003, respectively). The canal diameters and global angles did not differ between groups. Conclusion : Classifying cervical OPLL based on the presence of combined disc degeneration is beneficial for understanding the disorder's behavior. CC appears to be the main factor in the development of myelopathy in the pure group, whereas additional dynamic factors appear to affect its development in the degenerative group.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.20 no.1
• /
• pp.116-122
• /
• 2010

This paper proposes a method for classifying targets robust to geometric transformations of targets such as rotation, scale change, translation, and pose change. Targets which have rotation, scale change, and shift is firstly classified based on CM(Confidence Map) which is generated by similarity, scale ratio, and range of orientation for SIFT(Scale-Invariant Feature Transform) feature vectors. On the other hand, DB(DataBase) which is acquired in various angles is used to deal with pose variation of targets. Range of the angle is determined by comparing and analyzing the execution time and performance for sampling intervals. We experiment on various images which is geometrically changed to evaluate performance of proposed target classification method. Experimental results show that the proposed algorithm has a good classification performance.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.21 no.6
• /
• pp.155-161
• /
• 2021

Gesture recognition analytics through a camera in real time have been widely studied in recent years. Since a small number of features from human joints are extracted, low accuracy of classifying models is get in conventional gesture recognition studies. In this paper, CBAM (Convolutional Block Attention Module) with high accuracy for classifying images is proposed as a classification model and algorithm calculating the angle of joints depending on actions is presented to solve the issues. Employing five exercise gestures images from the fitness posture images provided by AI Hub, the images are applied to the classification model. Important 8-joint angles information for classifying the exercise gestures is extracted from the images by using MediaPipe, a graph-based framework provided by Google. Setting the features as input of the classification model, the classification model is learned. From the simulation results, it is confirmed that the exercise gestures are classified with high accuracy in the proposed model.

• Journal of the Korean Institute of Landscape Architecture
• /
• v.29 no.4
• /
• pp.12-23
• /
• 2001

The research focused on the landscape of mountains and hills drawn in a landscape picture. The purpose of the research is to classify patterns of landscape drawning in landscape pictures and to clarify the characteristics of the pattern by a quantitative index. We selected 21 landscape pictures to understand the Landscape Setting Here(LSH) and Landscape Setting These(LST). We investigated size quantitative indices using 1 landscape picture. The index is a follow: altitude, Visual Distance, Angles, Angle of Appearance Size, Inclination, and Angle of Incidence. The following results were obtained by using this data. 1) It has been understood that we offer an important city view because the LSH of this research can establish understanding of the city structure. 2) We dividing 3 patterns by the LST space drawn in the landscape picture. 3 patterns are Ferry point, Beauty point, an Signal-fire point. 3) We clarified the landscape characteristics of each pattern and the characteristics between patterns by using the index according to this pattern. 4) We understood the problem concerning the Seoul city landscape examining the pattern of this research with the ordinance of Seoul city. It is necessary to standardized a system of pattern classification utilized in landscape pictures to establish a universally interpreted detailed quantitative index, which can be applied to research.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.1
• /
• pp.93-100
• /
• 2019

One of critical issues in dealing with 360-degree realistic contents is the performance degradation in searching and recognition process since they support up to 4K UHD quality and have all image angles including the front, back, left, right, top, and bottom parts of a screen. To solve this problem, in this paper, we propose an efficient search and comparison method for 360-degree realistic contents. The proposed method first corrects the distortion at the less distorted regions such as front, left and right parts of the image excluding severely distorted regions such as upper and lower parts, and then it extracts feature points at the corrected region and selects the representative images through sequence classification. When the query image is inputted, the search results are provided through feature points comparison. The experimental results of the proposed method shows that it can solve the problem of performance deterioration when 360-degree realistic contents are recognized comparing with traditional 2D contents.

• Design & Manufacturing
• /
• v.15 no.3
• /
• pp.1-6
• /
• 2021

An artificial neural network model based on a deep learning algorithm is known to be more accurate than humans in image classification, but there is still a limit in the sense that there needs to be a lot of training data that can be called big data. Therefore, various techniques are being studied to build an artificial neural network model with high precision, even with small data. The transfer learning technique is assessed as an excellent alternative. As a result, the purpose of this study is to develop an artificial neural network system that can classify burr images of light guide plate products with 99% accuracy using transfer learning technique. Specifically, for the light guide plate product, 150 images of the normal product and the burr were taken at various angles, heights, positions, etc., respectively. Then, after the preprocessing of images such as thresholding and image augmentation, for a total of 3,300 images were generated. 2,970 images were separated for training, while the remaining 330 images were separated for model accuracy testing. For the transfer learning, a base model was developed using the NASNet-Large model that pre-trained 14 million ImageNet data. According to the final model accuracy test, the 99% accuracy in the image classification for training and test images was confirmed. Consequently, based on the results of this study, it is expected to help develop an integrated AI production management system by training not only the burr but also various defective images.

• International conference on construction engineering and project management
• /
• 2022.06a
• /
• pp.728-735
• /
• 2022

Roofers get exposed to increased risk of knee musculoskeletal disorders (MSDs) at different phases of a sloped shingle installation task. As different phases are associated with different risk levels, this study explored the application of machine learning for automated classification of seven phases in a shingle installation task using knee kinematics and roof slope information. An optical motion capture system was used to collect knee kinematics data from nine subjects who mimicked shingle installation on a slope-adjustable wooden platform. Four features were used in building a phase classification model. They were three knee joint rotation angles (i.e., flexion, abduction-adduction, and internal-external rotation) of the subjects, and the roof slope at which they operated. Three ensemble machine learning algorithms (i.e., random forests, decision trees, and k-nearest neighbors) were used for training and prediction. The simulations indicate that the k-nearest neighbor classifier provided the best performance, with an overall accuracy of 92.62%, demonstrating the considerable potential of machine learning methods in detecting shingle installation phases from workers knee joint rotation and roof slope information. This knowledge, with further investigation, may facilitate knee MSD risk identification among roofers and intervention development.