• KSCE Journal of Civil and Environmental Engineering Research
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• v.4 no.4
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• pp.113-125
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• 1984

Water supply has been mainly dependent on the construction of the dams in Korea. It is difficult, however, to continue to construct dams for many reasons, such as the decrease of construction sites, the increase of construction costs, the compensation of residents in flooded areas, and the environmental effects. Water demands have increased and are expected to continue increasing due to the concentration of people in the cities, the rise of the living standard, and rapid industrial growth. It is acutely important to find countermeasures such as development of ground water, desalination, and recycling of waste water to cope with increasing water demands. Recycling waste water includes all means of supplying non-potable water for their respective usages with proper water quality which is not the same quality as potable water. The usages of the recycled water include toilet flushing, air conditioning, car washing, yard watering, road cleaning, park sprinkling, and fire fighting, etc. Raw water for recycling is obtained from drainage water from buildings, toilets, and cooling towers, treated waste water, polluted rivers, ground water, reinfall, etc. The water quantity must be considered as well as its quality in selecting raw water for the recycling. The types of recycling may be classified roughly into closed recycle systems and open recycle systems, which can be further subdivided into individual recycle systems, regional recycle systems and large scale recycle system. The treatment methods of wastewater combine biochemical and physiochemical methods. The former includes activated sludge treatment, bio-disc treatment, and contact aeration treatment, and the latter contains sedimentation, sand filtration, activated carbon adsorption, ozone treatment, chlorination, and membrane filter. The recycling patterns in other countries were investigated and the effects of the recycling were divided into direct and indirect effects. The problems of water reuse in recycle patterns were also studied. The problems include technological, sanitary, and operational problems as well as cost and legislative ones. The duties of installation and administrative organization, structural standards for reuse of water, maintenance and financial disposal were also studied.

• Journal of Korean Society of Transportation
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• v.20 no.3
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• pp.149-158
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• 2002

For the purpose of preciously describing real time traffic pattern in urban road network, dynamic network loading(DNL) models able to simulate traffic behavior are required. A number of different methods are available, including macroscopic, microscopic dynamic network models, as well as analytical model. Equivalency minimization problem and Variation inequality problem are the analytical models, which include explicit mathematical travel cost function for describing traffic behaviors on the network. While microscopic simulation models move vehicles according to behavioral car-following and cell-transmission. However, DNL models embedding such travel time function have some limitations ; analytical model has lacking of describing traffic characteristics such as relations between flow and speed, between speed and density Microscopic simulation models are the most detailed and realistic, but they are difficult to calibrate and may not be the most practical tools for large-scale networks. To cope with such problems, this paper develops a new DNL model appropriate for dynamic traffic assignment(DTA), The model is combined with vertical queue model representing vehicles as vertical queues at the end of links. In order to compare and to assess the model, we use a contrived example network. From the numerical results, we found that the DNL model presented in the paper were able to describe traffic characteristics with reasonable amount of computing time. The model also showed good relationship between travel time and traffic flow and expressed the feature of backward turn at near capacity.

• Journal of Chest Surgery
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• v.38 no.1 s.246
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• pp.29-37
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• 2005

This study was prospectively designed to determine the physiologic effects of normothermic CPB and to compare its influences with hypothermic CPB. Material and Method: Thirty-six adult patients scheduled for el­ective cardiac surgery were randomly assigned to moderate hypothermic (hypothermic group nasopharyngeal tem­perature $26\~28^{\circ}C,\;n=18)$ ornormothermic (normothermic group, nasopharyngeal temperature > $35.5^{\circ}C\;n=18)$ CPB. Arterial blood samples were taken before CPB (Pre-CPB), 10 minutes after the start of CPB (CPB-10), and imme­diately after CPB stop (CPB-off) for determining total leukocyte counts, neuron-specific enolase (NSE), interleukin-6 (IL-6), endothelin-1 (ET-1), cortisol, troponin I (TNI), aspartate aminotransferase (AST), alanine aminotransferase (ALT), creatinine, blood urea nitrogen (BUN), and the pulmonary index $(Pi,\;PaO_{2}/FiO_{2}),$Other parameters such as urine output, mechanical ventilating period, ICU-staying period, postoperative complications and hospitalized days were also evaluated. Result: Total leukocyte counts, increased rate in NSE, in IL-6 and in cortisol at CPB-10 and CPB-off were significantly higher in normothermic group than in hyphothermic group. Urine output during CPB was lower in normothermic group than in hyphothermic group. The duration of mechanical ventilation, ICU-stay, and hospitalization were longer in normothermic group than in hyphothermic group. Conclusion: These findings sug­gested that normothermic CPB caused higher inflammatory and stress responses than hypothermic CPB during car­diac surgery using cold crystalloid cardioplegia. However, further studies with large number of cases should be carried out to validate this hypothesis.

• Journal of the Korean Institute of Landscape Architecture
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• v.40 no.5
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• pp.73-87
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• 2012

Nowadays, the Korean society is full of multiculturalism as there are many foreign ethnic enclaves. Many Chinese quarters are built in various parts of Korea along with the increasing population of Chinese immigrant. Especially, the Chinese quarter has shown the sign of time and the cultural characteristic of the local residents. This research is to study the market space of Chinese ethnic enclaves in Dairimdong. This research method is the field study to use a participant observation. Below are the research results: Chinese merchants put a private object such as "tanzi" on a sidewalk and install large awning covered full of sidewalk. Sidewalk transform from an outdoor space into an internal space because of Chinese merchants. Passers-by move to use vehicle roads and transform not only the car's space but also the passers-by space. Urban planners originally classify space into three categories, which are building - sidewalk - vehicles road. However, after Chinese came to the market, Chinese classified space into new three categories which is building - space for both sidewalk and "tanzi" - space for both sidewalk and vehicles road. New classification of space is quite different from the previous. In addition, Chinese thinks that the Dairimdong's Market is a very comfortable place. Because Dairimdong Market have many Chinese physical facilities. Next, Chinese thinks that the Dairimdong Market is a very friendly place to buy Chinese products easily. This market has become a place of consumption for the Chinese. Eventually, Dairimdong's Market has changed because of Chinese immigrants. It is possible to make satisfactory planning and design proposal to build Chinese quarters in the future through the explanation of space and status by way of culture. There are many careless mistakes in previous subjective planning and design proposal of the designers. Thus, it should consider the problems created by their way of use in later planning and design.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.129-152
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• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.