The study conducted statistical analysis of survey on the 239 workers participating in national Tower Crane installation/dismantlement in order to identify work environment and health hazard exposure of Tower Crane workers. Consequently for work related safety status of Tower Crane workers, the number of installation/dismantlement work was the highest at 15-20 times per month, and safety equipments were not arranged at work and they felt highly anxious when boarding on the Tower Crane. Furthermore, it is found that they feel anxious working in the influence of the weather, noise, vibration, wind, the sun ray, and structure characteristic. They have very low satisfaction in the insecureness of workplace and the work environment, and recognizes that their work affect highly on the health. 81.6% of workers have experienced accident at work, the cause of accidents were mainly due to poor work environment, and the health abnormality they have experienced were mostly fatigue and concentration reduction. For job stress, job demand and conflict in relationship were the highest sections. Total 66.5% of workers complained of musculoskeletal disorder related subjective symptom, and back pain patients were the highest of all groups.
While it is important to obtain the accurate information on snowfall data due to the increase in damage caused by the heavy snowfall in the winter season, it is not easy to observe the snowfall quantitatively. Recently, snow measurements using a weighing precipitation gauge have been carried out, but there is a problem that high snowfall intensity results in low accuracy. Also, the observed snowfall data are sensitive depending on wind speed, temperature, and humidity. In this study, a new process of quality control for snow water equivalent (SWE) data of the weighing precipitation gauge were proposed to cover the low accuracy of snow data and maximize the data utilization. Snowfall data (SWE) observed by Pluvio, Parsivel, snow-depth meter using laser or ultrasonic, and rainfall gauge in Cloud Physics Observation Site (CPOS) were compared and analyzed. Applying the QC algorithm including the use of number of hydrometeor particles as reference, the increased SWE per the unit time was determined and the data noise was removed and marked by flag. The SWE data converted by the number concentration of hydrometeor particles are tested as a method to restore the QC-removed data, and show good agreement with those of the weighing precipitation gauge, though requiring more case studies. The three events data for heavy snowfall disaster in Pyeongchang area was analyzed. The SWE data with improved quality was showed a good correlation with the eye-measured data ($R^2$ > 0.73).
Proceedings of the Korean Institute of Information and Commucation Sciences Conference
/
2019.05a
/
pp.341-343
/
2019
As the generation of high concentration particulate matter increases, much attention is focused on the prediction of particulate matter. Particulate matter refers to particulate matter less than $10{\mu}m$ diameter in the atmosphere and is affected by weather changes such as temperature, relative humidity and wind speed. Therefore, various studies have been conducted to analyze the correlation with weather information for particulate matter prediction. However, the nonlinear time series distribution of particulate matter increases the complexity of the prediction model and can lead to inaccurate predictions. In this paper, we try to mitigate the nonlinear characteristics of particulate matter by using cluster algorithm and classification algorithm of machine learning. The machine learning algorithms used are agglomerative clustering, density-based spatial clustering of applications with noise(DBSCAN).
Journal of the Architectural Institute of Korea Planning & Design
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v.35
no.8
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pp.23-34
/
2019
The façade, a fundamental function as a skin that protects human life from external environment such as cold and hot weather, snow, rain, and wind, etc, has served as a media for communication between indoor space of the building and outside space. From the media for communication point of view, the approach to envelope design, in which environmental elements are transmitted internally through the filtering of external environments, has been evolving in various ways from the past to the present. Today, modern architecture technologies including curtain wall systems and user-friendly computer programming and environmental analysis programs demonstrate a differentiated approach to envelope design related to the indoor environment. For this reason, it is worth noting that the envelope design factors and trends that appear variously in the UNStudio's projects before and after the 2000s. The factors reflected in the envelop design in conjunction with the indoor environment obtained through the case study of the UNStudio's office projects were daylight environment, thermal environment, ventilation, noise, privacy and view, and consideration for daylight environment and thermal environment was reflected in many cases through the case study. Looking at the changes in the diagrams in order of year, it can be seen that the envelope design using the environmental analysis tool has been performed since 2006. This is a clue to show the envelop design changes from the conceptual method to the data-based one. The diagrams and analysis results related to the envelop design showed that the thermal environment related to solar radiation was the most, and no diagrams and analysis related to the indoor illumination were found. Since 2010, PV panel installation has been shown in the envelope design, which can be found in the increased efficiency of PV panels due to the technological advances and the decrease in production cost.
Journal of the Korean Society of Environmental Restoration Technology
/
v.24
no.4
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pp.61-75
/
2021
Roadside greenery in the city is not only a means of reducing fine dust, but also an indispensable element of the city in various aspects such as improvement of urban thermal environment, noise reduction, ecosystem connectivity, and aesthetics. However, in studies dealing with the effect of reducing fine dust through trees in existing urban spaces, microscopic aspects such as the adsorption effect of plants were dealt with, structural changes such as the width of urban buildings and streets, and the presence or absence of trees, Impact studies that reflect the actual form of In this study, the effect of greenery composition applicable to urban space on PM2.5 was simulated through the microclimate epidemiologic model ENVI-met, and field measurements were performed in parallel to verify the results. In addition, by analyzing the results of fine dust background concentration, wind speed, and leaf area index, the sensitivity to major influencing variables was tested. As a result of the study, it was confirmed that the fine dust reduction effect was the highest in the case with a high planting amount, and the reduction effect was the greatest at a low background concentration. Based on this, the cost of planting street green areas and the effect of reducing PM2.5 were compared. The results of this study can contribute as a basis for considering the effect of pedestrian space on air quality when planning and designing street green spaces.
In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.
Bridge hangers, such as those in suspension and cable-stayed bridges, suffer from cumulative fatigue damage caused by dynamic loads (e.g., cyclic traffic and wind loads) in their service condition. Thus, the identification of damage to hangers is important in preserving the service life of the bridge structure. This study develops a new method for condition assessment of bridge hangers. The tension force of the bridge and the damages in the element level can be identified using the Bayesian optimization method. To improve the number of observed data, the additional mass method is combined the Bayesian optimization method. Numerical studies are presented to verify the accuracy and efficiency of the proposed method. The influence of different acquisition functions, which include expected improvement (EI), probability-of-improvement (PI), lower confidence bound (LCB), and expected improvement per second (EIPC), on the identification of damage to the bridge hanger is studied. Results show that the errors identified by the EI acquisition function are smaller than those identified by the other acquisition functions. The identification of the damage to the bridge hanger with various types of boundary conditions and different levels of measurement noise are also studied. Results show that both the severity of the damage and the tension force can be identified via the proposed method, thereby verifying the robustness of the proposed method. Compared to the genetic algorithm (GA), particle swarm optimization (PSO), and nonlinear least-square method (NLS), the Bayesian optimization (BO) performs best in identifying the structural damage and tension force.
Young-Sook Lee;Ram Singh;Geonhwa Jee;Young-Sil Kwak;Yong Ha Kim
Journal of Astronomy and Space Sciences
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v.40
no.3
/
pp.101-111
/
2023
We conducted a statistical study of polar mesospheric summer echoes (PMSEs) in relation to magnetic local time (MLT), considering the geomagnetic conditions using the K-index (or K). Additionally, we performed a case study to examine the velocity profile, specifically for high velocities (≥ ~100 m/s) varying with high temporal resolution at high K-index values. This study utilized the PMSE data obtained from the mesosphere-stratosphere-troposphere radar located in Esrange, Sweden (63.7°N, 21°E). The change in K-index in terms of MLT was high (K ≥ 4) from 23 to 04 MLT, estimated for the time PMSE was present. During the near-midnight period (0-4 MLT), both PMSE occurrence and signal-to-noise ratio (SNR) displayed an asymmetric structure with upper curves for K ≥ 3 and lower curves for K < 3. Furthermore, the occurrence of high velocities peaked at 3-4 MLT for K ≥ 3. From case studies focusing on the 0-3 MLT period, we observed persistent eastward-biased high velocities (≥ 200 m/s) prevailing for ~18 min. These high velocities were accompanied with the systematic motion of profiles at 85-88 km, including large shear formation. Importantly, the rapid variations observed in velocity could not be attributed to neutral wind effects. The present findings suggest a strong substorm influence on PMSE, especially in the midnight and early dawn sectors. The large zonal drift observed in PMSE were potentially energized by local electromagnetic fields or the global convection field induced by the electron precipitation during substorms.
This paper proposes an automatic indexing algorithm of golf video using audio information. In the proposed algorithm, the input audio stream is demultiplexed into the stream of video and audio. By means of Adaboost-cascade classifier, the continuous audio stream is classified into announcer's speech segment recorded in studio, music segment accompanied with players' names on TV screen, reaction segment of audience according to the play, reporter's speech segment with field background, filed noise segment like wind or waves. And golf swing sound including drive shot, iron shot, and putting shot is detected by the method of impulse onset detection and modulation spectrum verification. The detected swing and applause are used effectively to index action or highlight unit. Compared with video based semantic analysis, main advantage of the proposed system is its small computation requirement so that it facilitates to apply the technology to embedded consumer electronic devices for fast browsing.
The purpose of this study is to clarify the characteristics of the soundscape in a natural park and an urban park. The study sites were a natural park (Chiaksan Nationalpark) and an urban park (Rose Park) in Wonju City, Gangwon Province. Soundscape recording was conducted using Digital Recorder from April 2015 to January 2016. The analysis period was 8 days per season, with a total of 64 days (2 places). Analysis items were soundscape's daily cycle, soundscape type, and seasonal variation. According to the result of the daily cycle analysis of the soundscape, the natural park was dominated by the biophony in accordance with the cycle of the sun, and the airplane sound was observed in the daytime. Meanwhile, anthrophony was consistently produced in the urban park 24 hours a day. As a result of the detailed type analysis of the soundscape, the sources of biophony were classified into wild birds, mammals, insects and amphibians, and the sources of geophony were classified into rain and wind. The anthrophony was mostly airplane sound. In the urban park, wild birds appeared to most influence the biophonic sounds while rain and the wind were the most frequent sounds that contribute to geophony. The most influential components of anthrophony in the urban park were in the order of automobiles, people, music, construction, cleaning, and airplane sound. As a result of the seasonal difference analysis of the soundscape, it was statistically significant that the natural park shows higher biophony in spring, summer, and autumn compared to the urban park. Anthrophony in the urban park appeared to be higher than the natural park in all seasons. The significance of this study is that it is the first study to identify the characteristics of the soundscape of a natural park and an urban park emanating from different landscapes in South Korea.
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