• Smart Structures and Systems
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• v.18 no.2
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• pp.317-334
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• 2016

Structural Health Monitoring System (SHMS) works as an efficient platform for monitoring the health status and performance deterioration of engineering structures during long-term service periods. The objective of its installation is to provide reasonable suggestions for structural maintenance and management, and therefore ensure the structural safety based on the information extracted from the real-time measured data. In this paper, the SHMS implemented on a world-famous kilometer-level cable-stayed bridge, named as Sutong Cable-stayed Bridge (SCB), is introduced in detail. The composition and core functions of the SHMS on SCB are elaborately presented. The system consists of four main subsystems including sensory subsystem, data acquisition and transmission subsystem, data management and control subsystem and structural health evaluation subsystem. All of the four parts are decomposed to separately describe their own constitutions and connected to illustrate the systematic functions. Accordingly, the main techniques and strategies adopted in the SHMS establishment are presented and some extension researches based on structural health monitoring are discussed. The introduction of the SHMS on SCB is expected to provide references for the establishment of SHMSs on long-span bridges with similar features as well as the implementation of potential researches based on structural health monitoring.

• Journal of the Korean housing association
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• v.17 no.4
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• pp.37-46
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• 2006

The primary purpose of this study is to provide the managerial guidelines for the comprehensive apartment improvement planning. This plan will consider the time-serial apartment management plan. Through a questionnaire survey, residential satisfactions and demands on apartment units, apartment building and site amenities were investigated. Based on the statistical analysis, residents' demands were assessed. The resident groups were categorized into the three groups considering the apartments' life span where they were living. The results from the statistical analysis were finally compared with the long-term apartment management plan demonstrated in the Housing Code of the Ministry of Construction and Transportation. The major findings of this study were as follows. First, residential satisfaction on the equipments in apartment units was higher than that on the unit plans. Residents' satisfaction on the communal facilities in apartment sites was lower than that on the other factors. Thus, apartment unit plans and communal facilities in sites need to be improved. Second, though we had the three residents' groups, for the results of the residential satisfaction, the groups were divided into two groups: 'less than 10 years group'and 'over than 10 years group'. Considering the residents' demands for the apartment improvement according to the life span of apartment complexes, the habitability factor was demanded by 'the less than 10 years group' and the safety factor by 'the over than 10 years group'. Compared the residents' demands for apartment improvements with the long-term apartment management plan demonstrated in the Housing Code, the improvement cycles demanded by residents were shorter than those in the code. Thus, the management plan in the code should be reconsidered.

• Tunnel and Underground Space
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• v.28 no.2
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• pp.125-141
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• 2018

For a long-term mine development plan, the determination and design of mine tunnel size are very important because it is the basis of plans for equipment, transportation and operation. The ${\bigcirc}{\bigcirc}$ mine has had a difficulty in changing the mining plan due to the design of the tunnels with an emphasis on productivity improvement, and much effort was needed to maintain the mine tunnel. In this study, we designed the mine tunnel with optimized tunnel span considering the mechanical properties of rockmass and established the support plan. To do this, the estimation of the mechanical parameters(Swelling pressure, deformation coefficient and earth coefficient), field investigations and various analyses were carried out. As a result, it was necessary to consider the downsizing of the tunnel section in order to maintain the tunnel stability and dimension by using the roof bolt and analyzed that various functional constructions of the support material and method would be required to maintain the current tunnel size.

• Korean Journal of Social Welfare
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• v.61 no.2
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• pp.371-389
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• 2009

This study estimates the socioeconomic costs of missing children in Korea. The costs were classified as direct costs and indirect costs. The direct costs consisted of direct costs for searching for missing child such as making posters, transportation, and medical costs. The indirect costs were computed by the opportunity costs caused by child missing. The total costs that could be attributable to missing child were estimated to be about 570 million won per long-term missing child. This provides strong evidence that prevention of child missing is the most important and quick recovery after child is missing is the second most important. Missing child incurs substantial socioeconomic costs to the Korean society. Therefore, this study provides strong need for more interest from people who are indifferent to missing child issues and strong support for more government interventions to solve missing child problem in Korea. Further studies are needed to calculate socioeconomic costs of child missing more exactly.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.41 no.4
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• pp.313-319
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• 2017

Fiber optics sensors that have been mainly applied to aerospace areas are now finding applicability in other areas, such as transportation, including railways. Among the sensors, the fiber Bragg grating (FBG) sensors have led to a steep increase due to their properties of absolute measurement and multiplexing capability. Generally, the FBG sensors adhere to structures and sensing modules using adhesives such as an epoxy. However, the measurement errors that occurred when the FBG sensors were used in a long-term application, where they were exposed to environmental thermal load, required calibration. For this reason, the thermal curing of adhesives needs to be investigated to enhance the reliability of the FBG sensor system. This can be done at room temperature through cyclic thermal load tests using four types of specimens. From the test results, it is confirmed that residual compressive strain occurs to the FBG sensors due to an initial cyclic thermal load. In conclusion, signals of the FBG sensors need to be stabilized for applying them to a long-term SHM.

• Journal of Korea Trade
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• v.25 no.4
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• pp.17-36
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• 2021

Purpose - This study provides useful information to stakeholders by forecasting the tramp shipping market, which is a completely competitive market and has a huge fluctuation in freight rates due to low barriers to entry. Moreover, this study provides the most effective parameters for Baltic Dry Index (BDI) prediction and an optimal model by analyzing and comparing deep learning models such as the artificial neural network (ANN), recurrent neural network (RNN), and long short-term memory (LSTM). Design/methodology - This study uses various data models based on big data. The deep learning models considered are specialized for time series models. This study includes three perspectives to verify useful models in time series data by comparing prediction accuracy according to the selection of external variables and comparison between models. Findings - The BDI research reflecting the latest trends since 2015, using weekly data from 1995 to 2019 (25 years), is employed in this study. Additionally, we tried finding the best combination of BDI forecasts through the input of external factors such as supply, demand, raw materials, and economic aspects. Moreover, the combination of various unpredictable external variables and the fundamentals of supply and demand have sought to increase BDI prediction accuracy. Originality/value - Unlike previous studies, BDI forecasts reflect the latest stabilizing trends since 2015. Additionally, we look at the variation of the model's predictive accuracy according to the input of statistically validated variables. Moreover, we want to find the optimal model that minimizes the error value according to the parameter adjustment in the ANN model. Thus, this study helps future shipping stakeholders make decisions through BDI forecasts.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.321-327
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• 2023

With the development of artificial intelligence, the prediction system has become one of the essential technologies in our lives. Despite the growth of these technologies, traffic congestion at intersections in the 21st century has continued to be a problem. This paper proposes a system that predicts intersection traffic jams using a Convolutional LSTM (Conv-LSTM) algorithm. The proposed system models data obtained by learning traffic information by time zone at the intersection where traffic congestion occurs. Traffic congestion is predicted with traffic volume data recorded over time. Based on the predicted result, the intersection traffic signal is controlled and maintained at a constant traffic volume. Road congestion data was defined using VDS sensors, and each intersection was configured with a Conv-LSTM algorithm-based network system to facilitate traffic.

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

• Journal of Microbiology and Biotechnology
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• v.25 no.3
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• pp.366-374
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• 2015

Herein, we established a repeated-batch process for ethanol production from glycerol by immobilized Pachysolen tannophilus. The aim of this study was to develop a more practical and applicable ethanol production process for biofuel. In particular, using industrial-grade medium ingredients, the microaeration rate was optimized for maximization of the ethanol production, and the relevant metabolic parameters were then analyzed. The microaeration rate of 0.11 vvm, which is far lower than those occurring in a shaking flask culture, was found to be the optimal value for ethanol production from glycerol. In addition, it was found that, among those tested, Celite was a more appropriate carrier for the immobilization of P. tannophilus to induce production of ethanol from glycerol. Finally, through a repeated-batch culture, the ethanol yield (Y_e/g) of 0.126 ± 0.017 g-ethanol/g-glycerol (n = 4) was obtained, and this value was remarkably comparable with a previous report. In the future, it is expected that the results of this study will be applied for the development of a more practical and profitable long-term ethanol production process, thanks to the industrial-grade medium preparation, simple immobilization method, and easy repeated-batch operation.

• Journal of Science and Technology Studies
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• v.12 no.2
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• pp.81-116
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• 2012

Transition toward sustainablity being in progress in a variety of areas including climate, energy, household, transportation. This study analyzed system transition and backcasting of its management with case of sustainable transportation, foods, household system transition in the Netherlands. Backcasting has become a popular approach for sustainability taking a more reflexive perspective and looking back to the participatory experiments. In the Netherlands, participatory backcasting utilized as s innovative approach for long-term strategy for sustainability, based on stakeholder involvement, construction of normative sustainable futures, stakeholder leaning. In this study, we can be understood that transition management requires the participation and contribution of business, government, research and the public & public interest groups and backcating is tool leading to a sustainable future vision, stakeholder involvement. To create a new path toward sustainablity, it requires integrated consideration of related policy perspectives and participatory backcasting aiming at system innovations for influencing transitions.