• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.6
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• pp.50-55
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• 2011

The fluid power products are widely used in current industrial area such as automation of products and equipment assembly, high-tech machine tool, aircraft, train, and etc. As the development of industry is in progress, the development of the fluid power products is demanding and it is required in every industrial area. This research proposed a pneumatic system to evaluate displacement accuracy of the pneumatic actuator without external load and to analyze capability of integration of the valve system. The pneumatic system consisted of a combination of pneumatic actuator, four two-port valves, two three-port valves, two pressure valve, a check valve, two proximity sensors, and a program logic controller (PLC). The position controller is based on the PLC connected with the proximity sensors. The maximum air pressure applied for tests was $49.05N/cm^2$ and the displacement accuracy of a stroke was measured using a dial gauge. The supply- and discharge-side of air pressure and the length of the stroke of the pneumatic cylinder were varied The test of the position control of the pneumatic cylinder was carried out 50 times at each supply- and discharge-side air pressure of 24.53/34.34, 29.43/39.24, 34.34/44.15, and $39.24/49.05N/cm^2$ and replicated three times. The accuracy of the displacement of the pneumatic cylinder stroke increased as the supply- and discharge-side of air pressure increased with the stroke length of 133mm. Also the displacement accuracy increased as the stroke length increased with the fixed supply- and discharge-side of air pressure of the pneumatic cylinder as 34.34 and $44.15N/cm^2$, respectively. The most accurate displacement of the pneumatic cylinder was obtained at the supplyand discharge-side of air pressure of 39.24 and $49.05N/cm^2$, respectively, and strokes of 170 and 190mm.

• Journal of the Korean Society for Railway
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• v.20 no.5
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• pp.625-636
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• 2017

This study looked into the behavior of precast concrete track due to temperature variation and initial track deformation and examined the effect of initial deformation and deformation caused by temperature gradient on the stress distribution of slab under train load. In this paper, one of two papers in a series, a finite element analysis model for calculating deformation and stress of precast concrete track was proposed; the temperature distribution and displacements measured at the precast concrete track in the field were compared with the analytical results. The results show that the slab always curled up due to initial deformation; by comparing the measured displacements with the displacements calculated by taking measured temperatures at each depth as input, the effective built-in temperature (EBITD), the temperature difference between the top and bottom of the slab corresponding to the initial deformation, can be estimated. If EBITD is relevantly assumed, the calculated displacements correlate well with the measured ones.

• Journal of Biomedical Engineering Research
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• v.19 no.5
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• pp.469-476
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• 1998

In the case of acquisition and displaying medical Images such as ultrasonography and endoscopy on VGA monitor of PC system, image degradation of tear-drop appears through scan conversion. In this study, we compare several methods which can solve this degradation and implement the hardware system that resolves this problem in real-time with PC. It is possible to represent high quality image display and real-time processing and acquisition with specific de-interlacing device and PCI bridge on our hardware system. Image quality is improved remarkably on our hardware system. It is implemented as PC-based system, so acquiring, saving images and describing text comment on those images and PACS networking can be easily implemented.metabolism. All images were spatially normalized to MNI standard PET template and smoothed with 16mm FWHM Gaussian kernel using SPM96. Mean count in cerebral region was normalized. The VOls for 34 cerebral regions were previously defined on the standard template and 17 different counts of mirrored regions to hemispheric midline were extracted from spatially normalized images. A three-layer feed-forward error back-propagation neural network classifier with 7 input nodes and 3 output nodes was used. The network was trained to interpret metabolic patterns and produce identical diagnoses with those of expert viewers. The performance of the neural network was optimized by testing with 5~40 nodes in hidden layer. Randomly selected 40 images from each group were used to train the network and the remainders were used to test the learned network. The optimized neural network gave a maximum agreement rate of 80.3% with expert viewers. It used 20 hidden nodes and was trained for 1508 epochs. Also, neural network gave agreement rates of 75~80% with 10 or 30 nodes in hidden layer. We conclude that artificial neural network performed as well as human experts and could be potentially useful as clinical decision support tool for the localization of epileptogenic zones.

• Journal of Korean Society for Atmospheric Environment
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• v.32 no.2
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• pp.216-232
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• 2016

In present, the Seoul City is undergoing traffic congestion problems caused by rapid urbanization and population growth. Thus the City government has reorganized the mass transportation system since 2004 and the subway has become a very important means for public transit. Since the subway system is typically a closed environment, the indoor air quality issues have often raised by the public. Especially since a huge amount of PM (particulate matter) is emitted from ground tunnels passing through the subway train, it is now necessary to assess the characteristics and behaviors of fine PM inside the tunnel. In this study, the concentration patterns of $PM_1$, $PM_{2.5}$, and $PM_{10}$ in the Seoul subway line-2 were analyzed by real-time measurement during winter (Jan 13, 2015) and summer (Aug 7, 2015). The line-2 consisting of 51 stations is the most busy circular line in Seoul having the railway of 60.2 km length. The the one-day average $PM_{10}$ concentrations were $148{\mu}g/m^3$ in winter and $66.3{\mu}g/m^3$ in summer and $PM_{2.5}$ concentrations were $118{\mu}g/m^3$ and $58.5{\mu}g/m^3$, respectively. The $PM_{2.5}/PM_{10}$ ratio in the underground tunnel was lower than the outdoor ratio and also the ratio in summer is higher than in winter. Further the study examined structural types of underground subsections to explain the patterns of elevated PM concentrations in the line-2. The subsections showing high PM concentration have longer track, shorter curvature radius, and farther from the outdoor stations. We also estimated the outdoor PM concentrations near each station by a spatial statistical analysis using the $PM_{10}$ data obtained from the 40 Seoul Monitoring Sites, and further we calculated $PM_{2.5}/PM_{10}$ and $PM_1/PM_{10}$ mass ratios near the outdoor subway stations by using our observed outdoor $PM_1$, $PM_{2.5}$, and $PM_{10}$ data. Finally, we could develop pollution maps for outdoor $PM_1$ and $PM_{2.5}$ near the line-2 by using the kriging method in spatial analysis. This methodology may help to utilize existing $PM_{10}$ database when managing and control fine particle problems in Korea.

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

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.173-178
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• 2016

When a ballast track of a high-speed train is constructed on a bridge, the displacement of the bridge decks can occur because they are not fixed to the rails. Moreover, relative displacements occur between the bridge and rails caused by temperature changes and external loads. The current longitudinal resistance criteria (UIC Code 774-3, KR C-08080) on ballast tracks with continuous welded rails (CWRs) do not take into account the longitudinal movement of the bridge and the frictional force between the ballast and slabs. In addition, the magnitude of the longitudinal resistance, k, is calculated somewhat conservatively and, (therefore?) it acts as an unfavorable element in the design of long span and continuous railway bridges. Thus, in order to replicate the actual behavior more effectively, the longitudinal resistance of CWRs should take into account the additional rigidity between the slab and track. In this study, the longitudinal resistances of the ballasted track on the bridge were analyzed by carrying out an experimental study with a test setup designed to simulate the deck and bed track. In the test results, the maximum longitudinal resistances of the tests were similar to the resistances of the current codes, however, the measured longitudinal stiffness designed to limit the displacement of the tests were much smaller in comparison with the longitudinal stiffness on the codes.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.3
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• pp.471-479
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• 2018

Recently, the spread of smart TV based Android iOS Set Top box has become common. This paper propose a new approach to control the TV using gestures away from the era of controlling the TV using remote control. In this paper, the AdaBoost algorithm is applied to gesture recognition by using a mono camera. First, we use Camshift-based Body tracking and estimation algorithm based on Gaussian background removal for body coordinate extraction. Using global and local feature vectors, we recognized gestures with speed change. By tracking the time interval trajectories of hand and wrist, the AdaBoost algorithm with CART algorithm is used to train and classify gestures. The principal component feature vector with high classification success rate is searched using CART algorithm. As a result, 24 optimal feature vectors were found, which showed lower error rate (3.73%) and higher accuracy rate (95.17%) than the existing algorithm.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.5
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• pp.637-644
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• 2016

This paper is a follow-up to previous papers entitled, "Development of Tunnel-Environment Monitoring System and Its Installation" I [1] and II [2]. The target tunnel of these studies is the Solan Tunnel, which is a loop-type, single-track, 16.7-km-long tunnel located in mountainous terrain and passing through the Baekdudaegan mountain range. It is an ordinary railway tunnel designed for both freight and passenger trains. We analyzed the environmental conditions of the tunnel using temperature and humidity data recorded over approximately one year. The data were recorded using the Tunnel Rough Environment Measuring System (TREMS), which measures environmental data in subway and high-speed train tunnels and is installed in three locations inside the tunnel. Previous studies analyzed environmental conditions inside tunnels located in or near a city, whereas the tunnel in this study is located in a mountainous area. The tunnel conditions were compared with those measured outside the tunnel for each month. Hourly changes during summer and winter periods were also analyzed, and the environmental conditions at different locations inside the tunnel were compared. The results are widely applicable in studies on the thermal environment and air quality of tunnels, as well as for computer analysis of tunnel airflow such as tunnel ventilation and fire simulations.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.706-714
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• 2012

The Korea Meteorological Administration provided the recent four-years records of weather dataset for our very short-term heavy rainfall prediction. We divided the dataset into three parts: train, validation and test set. Through feature selection, we select only important features among 72 features to avoid significant increase of solution space that arises when growing exponentially with the dimensionality. We used a differential evolution algorithm and two classifiers as the fitness function of evolutionary computation to select more accurate feature subset. One of the classifiers is Support Vector Machine (SVM) that shows high performance, and the other is k-Nearest Neighbor (k-NN) that is fast in general. The test results of SVM were more prominent than those of k-NN in our experiments. Also we processed the weather data using undersampling and normalization techniques. The test results of our differential evolution algorithm performed about five times better than those using all features and about 1.36 times better than those using a genetic algorithm, which is the best known. Running times when using a genetic algorithm were about twenty times longer than those when using a differential evolution algorithm.

• Journal of Environmental Health Sciences
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• v.47 no.5
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• pp.410-424
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• 2021

Background: Subway stations have the characteristics of being located underground and are a representative public-use facility used by an unspecified number of people. As concerns about indoor air quality (IAQ) increase, various management measures are being implemented. However, there are few systematic studies and cases of long-term continuous measurement of underground station air quality. Objectives: The purpose of this study is to analyze changes and factors influencing IAQ in subway stations through real-time continuous long-term measurement using IoT-based IAQ sensing equipment, and to evaluate the IAQ improvement effect of a bio-filter system. Methods: The IAQ of a subway station in Seoul was measured using IoT-based sensing equipment. A bio-filter system was installed after collecting the background concentrations for about five months. Based on the data collected over about 21 months, changes in indoor air quality and influencing factors were analyzed and the reduction effect of the bio-filter system was evaluated. Results: As a result of the analysis, PM₁₀, PM_2.5, and CO₂ increased during rush hour according to the change in the number of passengers, and PM₁₀ and PM_2.5 concentrations were high when a PM warning/watch was issued. There was an effect of improving IAQ with the installation of the bio-filter system. The reduction rate of a new-bio-filter system with improved efficiency was higher than that of the existing bio-filter system. Factors affecting PM_2.5 in the subway station were the outdoor PM_2.5, platform PM_2.5, and the number of passengers. Conclusions: The IAQ in a subway station is affected by passengers, ventilation through the air supply and exhaust, and the spread of particulate matter generated by train operation. Based on these results, it is expected that IAQ can be efficiently improved if a bio-filter system with improved efficiency is developed in consideration of the factors affecting IAQ and proper placement.