• Smart Media Journal
• /
• v.5 no.2
• /
• pp.15-23
• /
• 2016

In this paper we propose the depth-poselets using body-part-poses and also propose the method to recognize the gesture. Since the gestures are composed of sequential poses, in order to recognize a gesture, it should emphasize to obtain the time series pose. Because of distortion and high degree of freedom, it is difficult to recognize pose correctly. So, in this paper we used partial pose for obtaining a feature of the pose correctly without full-body-pose. In this paper, we define the 16 gestures, a depth image using a learning image was generated based on the defined gestures. The depth poselets that were proposed in this paper consists of principal three-dimensional coordinates of the depth image and its depth image of the body part. In the training process after receiving the input defined gesture by using a depth camera in order to train the gesture, the depth poselets were generated by obtaining 3D joint coordinates. And part-gesture HMM were constructed using the depth poselets. In the testing process after receiving the input test image by using a depth camera in order to test, it extracts foreground and extracts the body part of the input image by comparing depth poselets. And we check part gestures for recognizing gesture by using result of applying HMM. We can recognize the gestures efficiently by using HMM, and the recognition rates could be confirmed about 89%.

• The Journal of the Convergence on Culture Technology
• /
• v.5 no.4
• /
• pp.387-394
• /
• 2019

In this study, in order to evaluate the damage and deterioration of the track components of sleeper floating track (STEDEF), the field samples(specimens) were taken from the serviced line over 20 years old, and the track components were visually inspected, and investigated by laboratory tests and finite element analysis. As a result of visual inspection, the damage of the rail pad and fastener was slight, but the rubber boot was worn and torn at the edges of bottom. The resilience pads were clearly examined for thickness reduction and fatigue hardening layer. As a result of spring stiffness test of rail pad and resilience pad, the deterioration of rail pad was insignificant, but the deterioration of resilience pad exceeded design standard value. Therefore resilience pad was directly affected by train passing tonnage. As a result of comparing the deterioration state of the field sample and the numerical analysis result, the stress and displacement concentration position of the finite element model and the damage position of the field sample were coincident.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.23 no.6
• /
• pp.628-638
• /
• 2017

In response to difficult employment conditions in the maritime industry and a desire to expand their career options, domestic mates are persuing DP operator training at institutions both domestically and abroad based on their shipboard experience. However, since the offshore plant service industry has not yet been established in Korea, those seeking to enter this field have difficulty acquiring qualifications and most seek work overseas for offshore shipping companies. Individuals wishing to work as DP operators are likely to face more conservative recruitment processes with overseas offshore shipping companies, focusing on career language restrictions as they will be non-native speakers relative to the foreign company, difficulty living in a multi-cultural environment, and lack of systematic information on essential job requirements. For these reasons, domestic mates have difficulty seeking jobs. Therefore, this study analyzes the capabilities and qualification required to be a DP operator to provide basic data for developing NCS standards representing a minimum level of competency. These standards can be applied by the government to develop plans for professional training for DP operators. In study, job classifications, competency standards and career development paths for DP operators have been proposed along with joint use of DP training vessels, to train specialized DP instructors. An NCS export model led by the government to activate professional training for DP operators is also presented.

• Journal of the Korea Society for Simulation
• /
• v.28 no.2
• /
• pp.1-14
• /
• 2019

A full scale hydrodynamic simulation that requires an accurate reproduction of shock-induced detonation was conducted for design of an energetic component system. A detailed hydrodynamic analysis SW was developed to validate the reactive flow model for predicting the shock propagation in a train configuration and to quantify the shock sensitivity of the energetic materials. The pyrotechnic device is composed of four main components, namely a donor unit (HNS+HMX), a bulkhead (STS), an acceptor explosive (RDX), and a propellant (BPN) for gas generation. The pressurized gases generated from the burning propellant were purged into a 10 cc release chamber for study of the inherent oscillatory flow induced by the interferences between shock and rarefaction waves. The pressure fluctuations measured from experiment and calculation were investigated to further validate the peculiar peak at specific characteristic frequency (${\omega}_c=8.3kHz$). In this paper, a step-by-step numerical description of detonation of high explosive components, deflagration of propellant component, and deformation of metal component is given in order to facilitate the proper implementation of the outlined formulation into a shock physics code for a full scale hydrodynamic simulation of the energetic component system.

• Journal of the Computational Structural Engineering Institute of Korea
• /
• v.35 no.2
• /
• pp.131-140
• /
• 2022

Installing Continuous Welded Rail (CWR) is one of the economical ways to resolve the challenges of noise, vibration, and the open-deck steel railway bridge impact, and the SSF method using the interlocking sleeper fastener has recently been developed. In this study, the method employed for determining the optimum vertical stiffness of the sleeper pad installed under the bridge sleeper, which is utilized to adjust the rail height and absorb shock when the train passes when the interlocking sleeper fastener is applied, is presented. To determine the optimal vertical stiffness of the sleeper pad, related existing design codes are reviewed, and, running safety, ride comfort, track safety, and bridge vibration according to the change in the vertical stiffness of the sleeper pad are estimated via flexible multi-body dynamic analysis,. The flexible multi-body dynamic analysis is performed using commercial programs ABAQUS and VI-Rail. The numerical analysis is conducted using the bridge model for a 30m-long plate girder bridge, and the response is calculated when passing ITX Saemaeul and KTX vehicles and freight wagon when the vertical stiffness of the sleeper pad is altered from 7.5 kN/mm to 240 kN/mm. The optimum stiffness of the sleeper pad is calculated as 200 kN/mm under the conditions of the track components applied to the numerical analysis.

• Journal of the Korea institute for structural maintenance and inspection
• /
• v.26 no.2
• /
• pp.28-36
• /
• 2022

As port structures are exposed to various extreme external loads such as wind (typhoons), sea waves, or collision with ships; it is important to evaluate the structural safety periodically. To monitor the port structure, especially the rubber fender, a fender segmentation system using a vision sensor and deep learning method has been proposed in this study. For fender segmentation, a new deep learning network that improves the encoder-decoder framework with the receptive field block convolution module inspired by the eccentric function of the human visual system into the DenseNet format has been proposed. In order to train the network, various fender images such as BP, V, cell, cylindrical, and tire-types have been collected, and the images are augmented by applying four augmentation methods such as elastic distortion, horizontal flip, color jitter, and affine transforms. The proposed algorithm has been trained and verified with the collected various types of fender images, and the performance results showed that the system precisely segmented in real time with high IoU rate (84%) and F1 score (90%) in comparison with the conventional segmentation model, VGG16 with U-net. The trained network has been applied to the real images taken at one port in Republic of Korea, and found that the fenders are segmented with high accuracy even with a small dataset.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.991-1005
• /
• 2022

Satellite imaging is an effective supplementary data source for detecting and verifying nuclear activity. It is also highly beneficial in regions with limited access and information, such as nuclear installations. Time series analysis, in particular, can identify the process of preparing for the conduction of a nuclear experiment, such as relocating equipment or changing facilities. Differences in the semantic segmentation findings of time series photos were employed in this work to detect changes in meaningful items connected to nuclear activity. Building, road, and small object datasets made of KOMPSAT 3/3A photos given by AIHub were used to train deep learning models such as U-Net, PSPNet, and Attention U-Net. To pick relevant models for targets, many model parameters were adjusted. The final change detection was carried out by including object information into the first change detection, which was obtained as the difference in semantic segmentation findings. The experiment findings demonstrated that the suggested approach could effectively identify altered pixels. Although the suggested approach is dependent on the accuracy of semantic segmentation findings, it is envisaged that as the dataset for the region of interest grows in the future, so will the relevant scope of the proposed method.

• Journal of the Korea Computer Graphics Society
• /
• v.28 no.3
• /
• pp.55-65
• /
• 2022

Recently, deep learning-based automated systems for identifying and detecting landmarks have been proposed. In order to train such a deep learning-based model without overfitting, a large amount of image and labeling data is required. Conventionally, an experienced reader manually identifies and labels landmarks in a patient's image. However, such measurement is not only expensive, but also has poor reproducibility, so the need for an automated labeling method has been raised. In addition, in the X-ray image, since various human tissues on the path through which the photons pass are displayed, it is difficult to identify the landmark compared to a general natural image or a 3D image modality image. In this study, we propose a geometric data augmentation technique that enables the generation of a large amount of labeling data in X-ray images. In addition, the optimal attention mechanism for landmark detection was presented through the implementation and application of various attention techniques to improve the detection performance of 16 major landmarks in the skull. Finally, among the major cranial landmarks, markers that ensure stable detection are derived, and these markers are expected to have high clinical application potential.

• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.625-640
• /
• 2022

Maglev rail joints are vital components serving as connections between the adjacent F-type rail sections in maglev guideway. Damage to maglev rail joints such as bolt looseness may result in rough suspension gap fluctuation, failure of suspension control, and even sudden clash between the electromagnets and F-type rail. The condition monitoring of maglev rail joints is therefore highly desirable to maintain safe operation of maglev. In this connection, an online damage detection approach based on three-dimensional (3D) convolutional neural network (CNN) and time-frequency characterization is developed for simultaneous detection of multiple damage of maglev rail joints in this paper. The training and testing data used for condition evaluation of maglev rail joints consist of two months of acceleration recordings, which were acquired in-situ from different rail joints by an integrated online monitoring system during a maglev train running on a test line. Short-time Fourier transform (STFT) method is applied to transform the raw monitoring data into time-frequency spectrograms (TFS). Three CNN architectures, i.e., small-sized CNN (S-CNN), middle-sized CNN (M-CNN), and large-sized CNN (L-CNN), are configured for trial calculation and the M-CNN model with excellent prediction accuracy and high computational efficiency is finally optioned for multiple damage detection of maglev rail joints. Results show that the rail joints in three different conditions (bolt-looseness-caused rail step, misalignment-caused lateral dislocation, and normal condition) are successfully identified by the proposed approach, even when using data collected from rail joints from which no data were used in the CNN training. The capability of the proposed method is further examined by using the data collected after the loosed bolts have been replaced. In addition, by comparison with the results of CNN using frequency spectrum and traditional neural network using TFS, the proposed TFS-CNN framework is proven more accurate and robust for multiple damage detection of maglev rail joints.

• Journal of Intelligence and Information Systems
• /
• v.28 no.4
• /
• pp.309-328
• /
• 2022

This study empirically analyzed a Korean pre-trained language models (PLMs) designed for natural language generation. The performance of two PLMs - BART and GPT - at the task of abstractive text summarization was compared. To investigate how performance depends on the characteristics of the inference data, ten different document types, containing six types of informational content and creation content, were considered. It was found that BART (which can both generate and understand natural language) performed better than GPT (which can only generate). Upon more detailed examination of the effect of inference data characteristics, the performance of GPT was found to be proportional to the length of the input text. However, even for the longest documents (with optimal GPT performance), BART still out-performed GPT, suggesting that the greatest influence on downstream performance is not the size of the training data or PLMs parameters but the structural suitability of the PLMs for the applied downstream task. The performance of different PLMs was also compared through analyzing parts of speech (POS) shares. BART's performance was inversely related to the proportion of prefixes, adjectives, adverbs and verbs but positively related to that of nouns. This result emphasizes the importance of taking the inference data's characteristics into account when fine-tuning a PLMs for its intended downstream task.