• Proceedings of the KSR Conference
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• 2004.10a
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• pp.127-132
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• 2004

The third double-track construction part of work, called Chung Ang Railroad line(Deok-So$\∼$Won-Ju) is in progress and the personal museum located 330m from the starting point of Pal-Dang Tunnel(length=4,470m) line in the canyon is to be effected by rock blasting during the tunnel excavation work, especially museum articles and building itself. This paper is the example of application suitable tunnel rock blasting pattern for excavation after the case study about the investigation and analysis of rock blasting noise pollution during tunnel excavation work. The museum is a three-story building, RC concrete structure and is located 17m from the top of the tunnel, in the center of the double-track line. Comparing estimate vibration frequency with site vibration one, it can be verified the reasonable rock blasting noise pollution as improving the application of tunnel excavation rock blasting pattern. The above pattern has been selected economically and effectively and applied to our construction field.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.10
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• pp.1044-1050
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• 2014

Robots used for pipe inspection have been studied for a long time and many mobile mechanisms have been proposed to achieve inspection tasks within pipelines. Localization is an important factor for an inpipe robot to perform successful autonomous operation. However, sensors such as GPS and beacons cannot be used because of the unique characteristics of inpipe conditions. In this paper, an inpipe localization algorithm based on elbow detection is presented. By processing the projected marker images of laser pointers and the attitude and heading data from an IMU, the odometer module of the robot determines whether the robot is within a straight pipe or an elbow and minimizes the integration error in the orientation. In addition, an off-line positioning algorithm has been performed with forward and backward estimation and Procrustes analysis. The experimental environment has consisted of several straight pipes and elbows, and a map of the pipeline has been constructed as the result.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.14 no.1
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• pp.65-75
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• 2002

The changes of sea bottom configuration, which may cause the coastal disasters, have been considered as social problems. It is obvious that the beach deformation is attributable to the sediment transport associated with erosion and siltation in coastal areas such esturies, channel and harbors. The prediction method and countermeasures far them, however, are not on the level of satisfaction, which indicates that make efforts should be made on developing them. Groin was constructed at Hak-Dong gravel beach to embark ship at 1996, as a result region of right of groin, severe erosion of beach is proceeding till now 1999. In this study, based on the field measurements, involved the one-line theory model which was selected for the prediction of shoreline change to prepare coastal protection methods of Hak-Deng gravel beach. Author found that the storaged sediment estimation model by Sonu and Beek(1971) is useful model at the Hak-Dong gravel beach by the use of topographical survey data from September, 1998 to September, 1999.

• Journal of Korea Spatial Information System Society
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• v.4 no.2 s.8
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• pp.55-64
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• 2002

VEfficient generation of road data is one of the most important issues in GIS (Geographic Information System). In this paper, we propose a hybrid approach for automatic generation of road data by combining mobile mapping and image processing techniques. Mobile mapping systems have a form of vehicle equipped with CCD camera, GPS, and INS. They can calculate absolute position of objects that appear in acquired image by photogrammetry, but it is labor-intensive and time-consuming. Automatic road detection methods have been studied also by image processing technology. However, the methods are likely to fail because of obstacles and exceptive conditions in the real world. To overcome the problems, we suggest a hybrid method for automatic road generation, by exploiting both GPS/INS data acquired by mobile mapping system and image processing algorithms. We design an estimator to estimate 3-D coordinates of road line and corresponding location in an image. The estimation process reduces complicated image processing operations that find road line. The missing coordinates of road line due to failure of estimation are obtained by cubic spline interpolation. The interpolation is done piecewise, separated by rapid change such as road intersection. We present experimental results of the suggested estimation and interpolation methods with image sequences acquired by mobile mapping system, and show that the methods are effective in generation of road data.

• Journal of Pharmacopuncture
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• v.20 no.3
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• pp.207-212
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• 2017

Objectives: Study of the mechanisms involved in cancer progression suggests that cyclooxygenase enzymes play an important role in the induction of inflammation, tumor formation, and metastasis of cancer cells. Thus, cyclooxygenase enzymes could be considered for cancer chemotherapy. Among these enzymes, cyclooxygenase 2 (COX-2) is associated with liver carcinogenesis. Various COX-2 inhibitors cause growth inhibition of human hepatocellular carcinoma cells, but many of them act in the COX-2 independent mechanism. Thus, the introduction of selective COX-2 inhibitors is necessary to achieve a clear result. The present study was aimed to determine the growth-inhibitory effects of new analogues of chalcone epoxide as selective COX-2 inhibitors on the human hepatocellular carcinoma (HepG2) cell line. Methods: Estimation of both cell growth and the amount of prostaglandin E2 (PGE2) production were used to study the effect of selective COX-2 inhibitors on the hepatocellular carcinoma cell. Cell growth determination has done by MTT assay in 24 h, 48 h and 72 h, and PGE2 production has estimated by using ELYSA kit in 48 h and 72 h. Results: The results showed growth inhibition of the HepG2 cell line in a concentration and time-dependent manner, as well as a reduction in the formation of PGE2 as a product of COX-2 activity. Among the compounds those analogues with methoxy and hydrogen group showed more inhibitory effect than others. Conclusion: The current in-vitro study indicates that the observed significant growth-inhibitory effect of chalcone-epoxide analogues on the HepG2 cell line may involve COX-dependent mechanisms and the PGE2 pathway parallel to the effect of celecoxib. It can be said that these analogues might be efficient compounds in chemotherapy of COX-2 dependent carcinoma specially preventing and treatment of hepatocellular carcinomas.

• Biotechnology and Bioprocess Engineering:BBE
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• v.7 no.5
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• pp.275-280
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• 2002

In order to control glucose concentration during fed-batch culture for antibiotic production, we applied so called “software sensor” which estimates unmeasured variable of interest from measured process variables using software. All data for analysis were collected from industrial scale cultures in a pharmaceutical company. First, we constructed an estimation model for glucose feed rate to keep glucose concentration at target value. In actual fed-batch culture, glucose concentration was kept at relatively high and measured once a day, and the glucose feed rate until the next measurement time was determined by an expert worker based on the actual consumption rate. Fuzzy neural network (FNN) was applied to construct the estimation model. From the simulation results using this model, the average error for glucose concentration was 0.88 g/L. The FNN model was also applied for a special culture to keep glucose concentration at low level. Selecting the optimal input variables, it was possible to simulate the culture with a low glucose concentration from the data sets of relatively high glucose concentration. Next, a simulation model to estimate time course of glucose concentration during one day was constructed using the on-line measurable process variables, since glucose concentration was only measured off-line once a day. Here, the recursive fuzzy neural network (RFNN) was applied for the simulation model. As the result of the simulation, average error of RFNN model was 0.91 g/L and this model was found to be useful to supervise the fed-batch culture.

• Water for future
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• v.28 no.5
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• pp.219-233
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• 1995

A non-stationary multivariate model is selected in which the mean and variance of rainfall are not temporally or spatially constant. And the rainfall prediction system is constructed which uses the recursive estimation algorithm, Kalman filter, to estimate system states and parameters of rainfall model simulataneously. The on-line, real-time, multivariate short-term, rainfall prediction for multi-stations and lead-times is carried out through the estimation of non-stationary mean and variance by the storm counter method, the normalized residual covariance and rainfall speed. The results of rainfall prediction system model agree with those generated by non-stationary multivariate model. The longer the lead time is, the larger the root mean square error becomes and the further the model efficiency decreases form 1. Thus, the accuracy of the rainfall prediction decreases as the lead time gets longer. Also it shows that the mean obtained by storm counter method constitutes the most significant part of the rainfall structure.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.6
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• pp.749-757
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• 2005

The present study analyzed actual cases of designed flow estimation method and designed flow rate of sewage pipe lines. In order to examine the effects of peak-hour demand factor estimation with given daily highest peak loading, we analyzed its effects on designed flow rate with changing the peak-hour demand factor from 2.0 to 10.0. The results of this study are as follows. When reviewing the recent designs, we found that 59.4% of pipe line with 250mm and 300mm diameter, which fall under minimum allowable pipeline did not meet the minimum velocity which is specified as 0.6m/sec in design standards. The pipe line that have minimal access population or have very low slope did not satisfy the minimum velocity. In estimating the designed sewage flow, the applied daily highest peak loading and hourly highest peaking loading were the load factor for the entire population of the planned area, and for the peak loading of the initial pipes connected to a very small population, we applied the same factor as that applied to the entire area and, as a result, the hourly highest flow was underestimated. Because, in case of the initial pipes, the method of applying the same peak loading to all subject areas is highly possible to produce underestimated design flow, when estimating the designed flow of the initial pipes connected to a small population need to adopt a rational flow factor according to the size of population. For this, it is considered to investigate and analyze raw data on daily and hourly variation of sewage flow.

• Journal of the Korea Computer Graphics Society
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• v.29 no.1
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• pp.1-11
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• 2023

Deep-learning-based image segmentation is one of the most widely employed lane detection approaches, and it requires a post-process for extracting the key points on the lanes. A general approach for key-point extraction is using a fixed threshold defined by a user. However, finding the best threshold is a manual process requiring much effort, and the best one can differ depending on the target data set (or an image). We propose a novel key-point extraction algorithm that automatically adapts to the target image without any manual threshold setting. In our adaptive key-point extraction algorithm, we propose a line-level normalization method to distinguish the lane region from the background clearly. Then, we extract a representative key point for each lane at a line (row of an image) using a kernel density estimation. To check the benefits of our approach, we applied our method to two lane-detection data sets, including TuSimple and CULane. As a result, our method achieved up to 1.80%p and 17.27% better results than using a fixed threshold in the perspectives of accuracy and distance error between the ground truth key-point and the predicted point.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2534-2546
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• 2021

Nuclear emergency preparedness and response is an essential part to ensure the safety of nuclear power plant (NPP). Key support technologies of nuclear emergency decision-making usually consist of accident diagnosis, source term estimation, accident consequence assessment, and protective action recommendation. Source term estimation is almost the most difficult part among them. For example, bad communication, incomplete information, as well as complicated accident scenario make it hard to determine the reactor status and estimate the source term timely in the Fukushima accident. Subsequently, it leads to the hard decision on how to take appropriate emergency response actions. Hence, this paper aims to develop a method for rapid source term estimation to support nuclear emergency decision making in pressurized water reactor NPP. The method aims to make our knowledge on NPP provide better support nuclear emergency. Firstly, this paper studies how to build a Bayesian network model for the NPP based on professional knowledge and engineering knowledge. This paper presents a method transforming the PRA model (event trees and fault trees) into a corresponding Bayesian network model. To solve the problem that some physical phenomena which are modeled as pivotal events in level 2 PRA, cannot find sensors associated directly with their occurrence, a weighted assignment approach based on expert assessment is proposed in this paper. Secondly, the monitoring data of NPP are provided to the Bayesian network model, the real-time status of pivotal events and initiating events can be determined based on the junction tree algorithm. Thirdly, since PRA knowledge can link the accident sequences to the possible release categories, the proposed method is capable to find the most likely release category for the candidate accidents scenarios, namely the source term. The probabilities of possible accident sequences and the source term are calculated. Finally, the prototype software is checked against several sets of accident scenario data which are generated by the simulator of AP1000-NPP, including large loss of coolant accident, loss of main feedwater, main steam line break, and steam generator tube rupture. The results show that the proposed method for rapid source term estimation under nuclear emergency decision making is promising.