• Journal of Korean Tunnelling and Underground Space Association
• /
• v.19 no.3
• /
• pp.375-388
• /
• 2017

In order to solve the traffic congest and environmental issues, small-cross section tunnel for small car only is increasing, but there is not standard for installation of disaster prevention facility. In this study, in order to investigate the behavioral characteristics of thermal environment and smoke in a small cross section tunnels with a large port exhaust ventilation system, the A86, the U-Smartway and the Seobu moterawy tunnel, Temperature and CO concentration in case of fire according to cross sectional area, heat release rate and exhaust air flow rate were analyzed by numerical analysis and the results were as follows. As the cross-sectional area of the tunnel decreases, the temperature of the fire zone increases and the rate of temperature rise is not significantly affected by heat release rate. However, there is a difference depending on the change of the exhaust air flow rate. In the case of applying the exhaust air flow rate $Q_3+2.5Ar$ of the large port exhaust ventilation system, the temperature of the fire zone was 7.1 times for A86 ($Ar=25.3m^2$) and 5.4 time for U-smartway ($Ar=37.32m^2$) by Seobu moterway tunnel ($Ar=46.67m^2$). The CO concentration of fire zone also showed the same tendency. The A86 tunnels were 10.7 times and the U-Smartways were 9.5 times more than the Seobu moterway. Therefore, in the case of a small section tunnel, the thermal environment and noxious gas concentration due to the reduction of the cross-sectional area are expected to increase significantly more than the cross-sectional reduction rate.

• Journal of Intelligence and Information Systems
• /
• v.17 no.3
• /
• pp.43-61
• /
• 2011

In this research, we propose a dynamic decision making using social context based on ontology. Dynamic adaptation is adopted for the high qualified decision making, which is defined as creation of proper information using contexts depending on decision maker's state of affairs in ubiquitous computing environment. Thereby, the context for the dynamic adaptation is classified as a static, dynamic and social context. Static context contains personal explicit information like demographic data. Dynamic context like weather or traffic information is provided by external information service provider. Finally, social context implies much more implicit knowledge such as social relationship than the other two-type context, but it is not easy to extract any implied tacit knowledge as well as generalized rules from the information. So, it was not easy for the social context to apply into dynamic adaptation. In this light, we tried the social context into the dynamic adaptation to generate context-appropriate personalized information. It is necessary to build modeling methodology to adopt dynamic adaptation using the context. The proposed context modeling used ontology and cases which are best to represent tacit and unstructured knowledge such as social context. Case-based reasoning and constraint satisfaction problem is applied into the dynamic decision making system for the dynamic adaption. Case-based reasoning is used case to represent the context including social, dynamic and static and to extract personalized knowledge from the personalized case-base. Constraint satisfaction problem is used when the selected case through the case-based reasoning needs dynamic adaptation, since it is usual to adapt the selected case because context can be changed timely according to environment status. The case-base reasoning adopts problem context for effective representation of static, dynamic and social context, which use a case structure with index and solution and problem ontology of decision maker. The case is stored in case-base as a repository of a decision maker's personal experience and knowledge. The constraint satisfaction problem use solution ontology which is extracted from collective intelligence which is generalized from solutions of decision makers. The solution ontology is retrieved to find proper solution depending on the decision maker's context when it is necessary. At the same time, dynamic adaptation is applied to adapt the selected case using solution ontology. The decision making process is comprised of following steps. First, whenever the system aware new context, the system converses the context into problem context ontology with case structure. Any context is defined by a case with a formal knowledge representation structure. Thereby, social context as implicit knowledge is also represented a formal form like a case. In addition, for the context modeling, ontology is also adopted. Second, we select a proper case as a decision making solution from decision maker's personal case-base. We convince that the selected case should be the best case depending on context related to decision maker's current status as well as decision maker's requirements. However, it is possible to change the environment and context around the decision maker and it is necessary to adapt the selected case. Third, if the selected case is not available or the decision maker doesn't satisfy according to the newly arrived context, then constraint satisfaction problem and solution ontology is applied to derive new solution for the decision maker. The constraint satisfaction problem uses to the previously selected case to adopt and solution ontology. The verification of the proposed methodology is processed by searching a meeting place according to the decision maker's requirements and context, the extracted solution shows the satisfaction depending on meeting purpose.

• Proceedings of the Korean Institute Of Construction Engineering and Management
• /
• autumn
• /
• pp.180-185
• /
• 2003

The increase of traffic over a bridge has been emerged as one of the most severe problems in view of bridge maintenance, since the load effect caused by the vehicle passage over the bridge has brought out a long-term damage to bridge structure, and it is nearly impossible to maintain operational serviceability of bridge to user's satisfactory level without any concern on bridge maintenance at the phase of completion. Moreover, bridge maintenance operation should be performed by regular inspection over the bridge to prevent structural malfunction or unexpected accidents front breaking out by monitoring on cracks or deformations during service. Therefore, technical breakthrough related to this uninterested field of bridge maintenance leading the public to the turning point of recognition is desperately needed. This study has the aim of development on automated inspection system to lower surface of bridge superstructures to replace the conventional system of bridge inspection with the naked eye, where the monitoring staff is directly on board to refractive or other type of maintenance .vehicles, with which it is expected that we can solve the problems essentially where the results of inspection are varied to change with subjective manlier from monitoring staff, increase stabilities in safety during the inspection, and make contribution to construct data base by providing objective and quantitative data and materials through image processing method over data captured by cameras. By this system it is also expected that objective estimation over the right time of maintenance and reinforcement work will lead enormous decrease in maintenance cost.

• Korean Journal of Agricultural and Forest Meteorology
• /
• v.3 no.3
• /
• pp.135-142
• /
• 2001

This study attempts to categorizes the factors of forest fire occurrences based on regional meteorologic data and general forest no characteristics of 18 cities and guns in Kangwon province. lo accomplish this goal, some statistical analyses such as analysis of variance, correspondence analysis and multidimensional scaling were adopted. To reveal the forest fires pattern of study region, a categorization process was conducted by employing the quantification approach which modified and quantified the metric-data of fire occurrence dates. Also, The fire occurrence similarity was compared by using multidimensional scaling for each study region. The major results are summarized as follows: It was found that the meteorological factors emerged as different to each region are average and maximum temperature, minimum dew point temperature and average and maximum wind speed. In the result of correspondence analysis representing relationships between fire causes and study regions, Kangrung is caused by arsonist, Chulwon, Hwachen and Yanggu caused by military factor, Sokcho and Chunchen caused by the debris burning, and Samchuk caused by general man-caused fires, respectively. Finally, the forest fire occurrence pattern of this study regions were divided into five areas such as, group I including Samchuk, Kangryung, Chunchen, Wonju, Hongchen and Hhoingsung, group II including Donghae, Taebaek, Yangyang and Pyongchang, group III including Jungsun, Chulwon and Whachen, group Ⅵ including Gosung, Injae and Yanggu, and group V including Shokcho and Youngwol.

• Journal of Korean Society of Transportation
• /
• v.23 no.5 s.83
• /
• pp.47-55
• /
• 2005

Roads deeply affect the life of people and keep doing an important role to support economic growth of a country. According to the budget plan of the ministry of construction and transportation of Korea, 8.1 trillion won have been allotted for road investment in the year of 2002 which occupy 61% of the transportation infrastructure special account (13.3 trillion won) and 4.7% of the total national budget (1,740 trillion won). It is true that services generated from road investment such as mobility enhancement and increased accessibility have shown positive effects through shortened travel time and decreased vehicle operating cost. However, it is also notable that many negative effects are gradually being discussed and those are nowadays getting severer due to enhanced people interests about road construction, increased concerns on environment and active public involvement that were evoked by traffic accidents, air pollution & noise and destruction of environment. Road construction processes in Korea are normally governed by administrative sectors (suppliers) not by users. These processes ate very weak to accomodate user s needs and community concerns thus easy to fail finalizing a road project without hassles. A public hearing process is supposed to be held in the processes of detailed design step and the environmental impact analysis. However, it is not enough to grab user's needs and community concerns. Increased public involvement frequencies, optimized public involvement timing and enhanced depth of public involvement magnitude are suggested to improve the current poor public involvement schemes in road planning and design processes. The application of these recommended methods to the road planning and design processes may guarantee the change from the current supplier-oriented schemes to the new user-oriented one. Also, this study suggests to reset objectivity and clarity of road construction process, to make conciliation guidelines based on many practical cases that produced good results, to introduce public involvement techniques in a stepwise basis, and to foster the professionals via education and training programs.

• Journal of agricultural medicine and community health
• /
• v.24 no.1
• /
• pp.65-77
• /
• 1999

In order to estimate the utilization pattern of a rural health subcenter, and to identify the recognition for it among the farmhouse members in a suburban area, a questionnaire survey was carried out for objects of 696 population. The results observed were as follows: The annual utilization rate of rural health subcenter for a basic health service unit was 25.0 per 100 persons, and annual mean visiting times was 0.22 times. And the most frequent disease by annual health subcenter utilization illness was musculoskeletal disease(30.6%), and the next was respiratory disease(14.1%), gastrointestinal disease(13.9%) by order. Favorite reason for community health subcenter utilization were near distance from living place(49.6%), lower disease severity(18.9%) and lower medical cost(18.1%) by order. But disfavoring reasons for it were absence of specialist(20.2%), non effective treatment(19.2%) and insufficient equipment(14.7%) by order. And insufficient items about community health subcenter utilization were restriction of treatment limit(40.7%), lower reliance(22.5%) and difficulty in traffic(13.4%) by order. The results of logistic regression analysis suggested that statistically significant factors in health subcenter utilization was educational level. The desirable works for the health subcenter in a suburban area were disease control of elderly and disease preventing service. These results suggested that to increase the utilization of rural health subcenter in a suburban area and to promote the accessibility of rural residents to primary health care, there must be considered public relation about health subcenter, improvement of medical quality and change of priority about health subcenter's works.

• The KIPS Transactions:PartA
• /
• v.13A no.2 s.99
• /
• pp.123-136
• /
• 2006

The prefetching technique is an effective way to reduce the latency caused memory access. However, excessively aggressive prefetch not only leads to cache pollution so as to cancel out the benefits of prefetch but also increase bus traffic leading to overall performance degradation. In this thesis, a prefetch filtering scheme is proposed which dynamically decides whether to commence prefetching by referring a filtering table to reduce the cache pollution due to unnecessary prefetches In this thesis, First, prefetch hashing table 1bitSC filtering scheme(PHT1bSC) has been shown to analyze the lock problem of the conventional scheme, this scheme such as conventional scheme used to be N:1 mapping, but it has the two state to 1bit value of each entries. A complete block address table filtering scheme(CBAT) has been introduced to be used as a reference for the comparative study. A prefetch block address lookup table scheme(PBALT) has been proposed as the main idea of this paper which exhibits the most exact filtering performance. This scheme has a length of the table the same as the PHT1bSC scheme, the contents of each entry have the fields the same as CBAT scheme recently, never referenced data block address has been 1:1 mapping a entry of the filter table. On commonly used prefetch schemes and general benchmarks and multimedia programs simulates change cache parameters. The PBALT scheme compared with no filtering has shown enhanced the greatest 22%, the cache miss ratio has been decreased by 7.9% by virtue of enhanced filtering accuracy compared with conventional PHT2bSC. The MADT of the proposed PBALT scheme has been decreased by 6.1% compared with conventional schemes to reduce the total execution time.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.19 no.1
• /
• pp.95-107
• /
• 2017

In this study, a preliminary study was undertaken for development of a tunnel incident automatic detection system based on a machine learning algorithm which is to detect a number of incidents taking place in tunnel in real time and also to be able to identify the type of incident. Two road sites where CCTVs are operating have been selected and a part of CCTV images are treated to produce sets of training data. The data sets are composed of position and time information of moving objects on CCTV screen which are extracted by initially detecting and tracking of incoming objects into CCTV screen by using a conventional image processing technique available in this study. And the data sets are matched with 6 categories of events such as lane change, stoping, etc which are also involved in the training data sets. The training data are learnt by a resilience neural network where two hidden layers are applied and 9 architectural models are set up for parametric studies, from which the architectural model, 300(first hidden layer)-150(second hidden layer) is found to be optimum in highest accuracy with respect to training data as well as testing data not used for training. From this study, it was shown that the highly variable and complex traffic and incident features could be well identified without any definition of feature regulation by using a concept of machine learning. In addition, detection capability and accuracy of the machine learning based system will be automatically enhanced as much as big data of CCTV images in tunnel becomes rich.

• Journal of Intelligence and Information Systems
• /
• v.26 no.2
• /
• pp.131-145
• /
• 2020

In line with the trend of industrial innovation, IoT technology utilized in a variety of fields is emerging as a key element in creation of new business models and the provision of user-friendly services through the combination of big data. The accumulated data from devices with the Internet-of-Things (IoT) is being used in many ways to build a convenience-based smart system as it can provide customized intelligent systems through user environment and pattern analysis. Recently, it has been applied to innovation in the public domain and has been using it for smart city and smart transportation, such as solving traffic and crime problems using CCTV. In particular, it is necessary to comprehensively consider the easiness of securing real-time service data and the stability of security when planning underground services or establishing movement amount control information system to enhance citizens' or commuters' convenience in circumstances with the congestion of public transportation such as subways, urban railways, etc. However, previous studies that utilize image data have limitations in reducing the performance of object detection under private issue and abnormal conditions. The IoT device-based sensor data used in this study is free from private issue because it does not require identification for individuals, and can be effectively utilized to build intelligent public services for unspecified people. Especially, sensor data stored by the IoT device need not be identified to an individual, and can be effectively utilized for constructing intelligent public services for many and unspecified people as data free form private issue. We utilize the IoT-based infrared sensor devices for an intelligent pedestrian tracking system in metro service which many people use on a daily basis and temperature data measured by sensors are therein transmitted in real time. The experimental environment for collecting data detected in real time from sensors was established for the equally-spaced midpoints of 4×4 upper parts in the ceiling of subway entrances where the actual movement amount of passengers is high, and it measured the temperature change for objects entering and leaving the detection spots. The measured data have gone through a preprocessing in which the reference values for 16 different areas are set and the difference values between the temperatures in 16 distinct areas and their reference values per unit of time are calculated. This corresponds to the methodology that maximizes movement within the detection area. In addition, the size of the data was increased by 10 times in order to more sensitively reflect the difference in temperature by area. For example, if the temperature data collected from the sensor at a given time were 28.5℃, the data analysis was conducted by changing the value to 285. As above, the data collected from sensors have the characteristics of time series data and image data with 4×4 resolution. Reflecting the characteristics of the measured, preprocessed data, we finally propose a hybrid algorithm that combines CNN in superior performance for image classification and LSTM, especially suitable for analyzing time series data, as referred to CNN-LSTM (Convolutional Neural Network-Long Short Term Memory). In the study, the CNN-LSTM algorithm is used to predict the number of passing persons in one of 4×4 detection areas. We verified the validation of the proposed model by taking performance comparison with other artificial intelligence algorithms such as Multi-Layer Perceptron (MLP), Long Short Term Memory (LSTM) and RNN-LSTM (Recurrent Neural Network-Long Short Term Memory). As a result of the experiment, proposed CNN-LSTM hybrid model compared to MLP, LSTM and RNN-LSTM has the best predictive performance. By utilizing the proposed devices and models, it is expected various metro services will be provided with no illegal issue about the personal information such as real-time monitoring of public transport facilities and emergency situation response services on the basis of congestion. However, the data have been collected by selecting one side of the entrances as the subject of analysis, and the data collected for a short period of time have been applied to the prediction. There exists the limitation that the verification of application in other environments needs to be carried out. In the future, it is expected that more reliability will be provided for the proposed model if experimental data is sufficiently collected in various environments or if learning data is further configured by measuring data in other sensors.