• Journal of Korean Society of Water and Wastewater
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• v.18 no.6
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• pp.801-806
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• 2004

The pilot study was conducted to (i) investigate the ability of various membrane integrity monitoring methods to detect changes in membrane integrity during operation, and (ii) determine the impact of membrane damage on microbial removal by microfiltration. Two variations of air pressure hold tests were investigated for direct integrity monitoring: pressure decay (PD) and diffusive air flow (DAF) tests which are most commonly used integrity tests for microfiltration (MF) membranes. Both PD and DAF tests were sensitive enough to detect one damaged fiber out of 66,000 under field operaing conditions. Indirect integrity monitoring such as turbidity and particle counting, however, responded poorly to defects in membrane systems. Microbial challenge study was performed using both new and deliberately damaged membranes, as well as varying the state of fouling of the membrane. This study demonstrated that MF membrane with nominal pore size $0.2{\mu}m$ was capable of removing various pathogens including coliform, spore, and cryptosporidium, at the level required by drinking water regulations, even when high operating pressures were applied. A sharp decrease in average log reduction value (LRV) was observed when one fiber was damaged, emphasizing the importance of membrane integrity in control of microbial contamination.

• Journal of Korean Society of Water and Wastewater
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• v.21 no.2
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• pp.243-252
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• 2007

Effects of alum dosage on the particle growth were investigated by monitoring particle counts in a rapid mixing process. Kaolin was used for turbid water sample and several other chemicals were added to adjust pH and ionic strength. The range of velocity gradient and mixing time applied for rapid mixing were $200{\sim}300sec^{-1}$ and 30~180 sec, respectively. Particle distribution in the synthetic water sample was close to the natural water where their turbidity was same. The number of particles in the range of $10.0{\sim}12.0{\mu}m$ increased rapidly with rapid mixing time at alum dose of 20mg/L, however, the number of $8.0{\sim}9.0{\mu}m$ particles increased at alum dose of 50mg/L. The number of $14.0{\sim}25.0{\mu}m$ particles at alum dose of 20mg/L was 10 times higher than them at alum dose of 50mg/L. Dominant particle growth was monitored at the lower alum dose than the optimum dose from a jar test at an extended rapid mixing time(about 120 sec). The number of $8.0{\sim}14.0{\mu}m$ particles was lower both at a higher alum doses and higher G values. At G value of $200sec^{-1}$ and at alum dose of 10-20mg/L, residual turbidity was lower as the mixing time increased. But at alum dose above 40mg/L and at same G value, lower residual turbidity occurred in a short rapid mixing time. Low residual turbidity at G value of $300sec^{-1}$ occurred both at lower alum doses and at shorter mixing time comparing to the results at G value of $200sec^{-1}$.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.2
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• pp.255-260
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• 2006

Safety-critical navigation systems have to provide 'reliable' position solutions, i.e., they must detect and exclude measurement or system faults and estimate the uncertainty of the solution. To obtain more accurate and reliable navigation systems, various filtering methods have been employed to reduce measurement noise level, or integrate sensors, such as global navigation satellite system/inertial navigation system (GNSS/INS) integration. Recently, particle filters have attracted attention, because they can deal with nonlinear/non-Gaussian systems. In most GNSS applications, the GNSS measurement noise is assumed to follow a Gaussian distribution, but this is not true. Therefore, we have proposed a fault detection and exclusion method using particle filters assuming non-Gaussian measurement noise. The performance of our method was contrasted with that of conventional Kalman filter methods with an assumed Gaussian noise. Since the Kalman filters presume that measurement noise follows a Gaussian distribution, they used an overbounded standard deviation to represent the measurement noise distribution, and since the overbound standard deviations were too conservative compared to the actual distributions, this degraded the integrity-monitoring performance of the filters. A simulation was performed to show the improvement in performance of our proposed particle filter method by not using the sigma overbounding. The results show that our method could detect smaller measurement biases and reduced the protection level by 30% versus the Kalman filter method based on an overbound sigma, which motivates us to use an actual noise model instead of the overbounding or improve the overbounding methods.

• Tunnel and Underground Space
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• v.31 no.6
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• pp.508-519
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• 2021

In this study, a model was developed to predict the peak particle velocity (PPV) that affects people and the surrounding environment during blasting. Four machine learning models using the k-nearest neighbors (kNN), classification and regression tree (CART), support vector regression (SVR), and particle swarm optimization (PSO)-SVR algorithms were developed and compared with each other to predict the PPV. Mt. Yogmang located in Changwon-si, Gyeongsangnam-do was selected as a study area, and 1048 blasting data were acquired to train the machine learning models. The blasting data consisted of hole length, burden, spacing, maximum charge per delay, powder factor, number of holes, ratio of emulsion, monitoring distance and PPV. To evaluate the performance of the trained models, the mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE) were used. The PSO-SVR model showed superior performance with MAE, MSE and RMSE of 0.0348, 0.0021 and 0.0458, respectively. Finally, a method was proposed to predict the degree of influence on the surrounding environment using the developed machine learning models.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.20 no.4
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• pp.248-255
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• 2010

Airborne nanoparticle concentrations in three metrics (particle surface area concentration, particle number concentration, and particle mass concentrations) were measured by task in a laboratory making carbon nanotubes (CNTs) films using three direct reading instruments. Because of the conducted other researcher's experiment before the tasks, airborne nanoparticle surface area and number concentrations are the highest at the first time conducted weighing and mixing by sonication task, respectively. Because of the mist generated during mixing by sonication, the highest airborne nanoparticle surface area and PM1 concentrations were measured in the task among the total. Nanoparticle surface area concentrations at the researchers' breathing zones had high correlation (r=0.93, p<0.01) with those measured at an area in the laboratory. This result indicates that nanoparticles generated during the experiment contaminated the whole room air. When the experiment performed all the fume hoods weren't operated and making CNTs films task were conducted in the out of the fume hoods. In conclusion, researchers performing making CNTs film experiments were exposed to airborne nanoparticles generated during the experiment without adequate controls. We recommend that adequate controls should be implemented so that workers' exposures to airborne nanoparticle are limited to minimum levels.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.24 no.10 s.181
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• pp.2420-2427
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• 2000

In contacting between surface, there is wear and the generation of wear particles. The particles contained in the lubricating oil carry detailed and important information about the condition monitoring of the machine. Therefore, This paper was undertaken for Ferrography system of wear debris generated from lubricated moving machine surface. The lubricating wear test was performed under different experimental conditions using the Falex wear test of Pin and V-Block type by Ti(C,N) coated. It was shown from the test results that wear particle concentration(WPC) ; wear severity Index(IS) and size\distribution have come out all the higher value by increases sliding friction time. By the Ferrogram a thin leaf wear debris as well as ball and plate type wear particles was observed.

• Journal of Auto-vehicle Safety Association
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• v.14 no.3
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• pp.71-76
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• 2022

In autonomous driving cars, 3D pose estimation can be one of the effective methods to enhance safety control for OOP (Out of Position) passengers. There have been many studies on human pose estimation using a camera. Previous methods, however, have limitations in automotive applications. Due to unexplainable failures, CNN methods are unreliable, and other methods perform poorly. This paper proposes robust real-time multi-human 3D pose estimation architecture in vehicle using monocular RGB camera. Using particle filter, our approach integrates CNN 2D/3D pose measurements with available information in vehicle. Computer simulations were performed to confirm the accuracy and robustness of the proposed algorithm.

• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.143-149
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• 1997

This paper was undertaken to do image analysis of wear debris on operating condition of the lubricated moving surfaces. This lubricating wear test was performed under different experimental conditions using the wear test device was made in our laboratory and wear testing specimen of the pin on dist type was rubbed in paraffine series base oil, by materials, varying applied load, sliding distance. The four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) to describe wear debris have been developed and are outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology for machine condition monitoring, this to overcome many of the difficulties with current methods and facilitating wider use of wear particle analysis in machine condition monitouing.

• Journal of the Korean Society of Industry Convergence
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• v.27 no.2_2
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• pp.319-324
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• 2024

In this paper, the Artificial Intelligence Space(AI-Space) for human-robot interface is presented, which can enable human-computer interfacing, networked camera conferencing, industrial monitoring, service and training applications. We present a method for representing, tracking, and objects(human, robot, chair) following by fusing distributed multiple vision systems in AI-Space. The article presents the integration of color distributions into particle filtering. Particle filters provide a robust tracking framework under ambiguous conditions. We propose to track the moving objects(human, robot, chair) by generating hypotheses not in the image plane but on the top-view reconstruction of the scene.

• Journal of Environmental Impact Assessment
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• v.27 no.4
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• pp.378-393
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• 2018

As atmospheric fine dust problems in Korea become more serious, there are growing needs to find the concentration of fine particles in indoor and outdoor areas and there are increasing demands for sensor-based portable monitoring devices capable of measuring fine dust concentrations instantly. The low-cost portable monitoring devices have been widely manufactured and used without the prescribed certification standards which would cause unnecessary confusion to the concerned public. To evaluate the reliability those devices and to improve their usability, following studies were conducted in this work; 1) The comparisons between sensor-based devices and comparison with more accurate devices were performed. 2) Several experiments were conducted to understand usefulness of the portable monitoring devices. As results, the absolute concentration levels need to be adjusted due to insensitivity of the tiny light scattering sensors in the portable devices, but their linearity and reproducibility seem to be acceptable. By using those monitoring devices, users are expected to have benefits of recognizing the changes of concentration more quickly and could help preventing themselves from the adverse health impacts.