• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2017.05a
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• pp.255-257
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• 2017

These days automotive markets are launching models that include a variety of IT technologies. Tesla's Tesla model S and Google's unmanned automobiles are emerging one after another. This type of automobile with IT technology provides various convenience to the driver and the driver is getting benefit by various conveience services. on the contrary, it is also true that defects for errors in electronic components cause accidents that threaten the safety of drivers. There is a sudden unintended acceleration among these accidents. The cause of the accident is not clear yet, but the claim that the ECU device caused by the magnetic field causes accident of the car due is the most reliable. But, in Korea, when occur a car sudden unintended acceleration accident, the char maker often claims that an accident occurred due to driver's pedal malfunction. Also most drivers are responsible for the lack of grounds to refute. In this paper, the pedal operation image of the driver is acquired and the sensor is attached to the control part such as the excel and brake so as to discriminate whether the vehicle sudden unintended acceleration accident is the driver's pedal operation error or the fault of. i have implemented a system that can do this.

• Journal of Intelligence and Information Systems
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• v.9 no.3
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• pp.23-47
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• 2003

Traditional studies for customer relationship management (CRM) generally focus on static CRM in a specific time frame. The static CRM and customer behavior knowledge derived could help marketers to redirect marketing resources fur profit gain at that given point in time. However, as time goes, the static knowledge becomes obsolete. Therefore, application of CRM to an online retailer should be done dynamically in time. Customer-based analysis should observe the past purchase behavior of customers to understand their current and likely future purchase patterns in consumer markets, and to divide a market into distinct subsets of customers, any of which may conceivably be selected as a market target to be reached with a distinct marketing mix. Though the concept of buying-behavior-based CRM was advanced several decades ago, virtually little application of the dynamic CRM has been reported to date. In this paper, we propose a dynamic CRM model utilizing data mining and a Monitoring Agent System (MAS) to extract longitudinal knowledge from the customer data and to analyze customer behavior patterns over time for the Internet retailer. The proposed model includes an extensive analysis about a customer career path that observes behaviors of segment shifts of each customer: prediction of customer careers, identification of dominant career paths that most customers show and their managerial implications, and about the evolution of customer segments over time. furthermore, we show that dynamic CRM could be useful for solving several managerial problems which any retailers may face.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.123-134
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• 2021

This study was conducted for the purpose of convergence exploration of evidence-based practices for the advancement of performance analysis of the extracurricular activities at Duksung Women's University. The research method consisted of an expert meeting procedure along with a procedure for analyzing previous studies dealing with the performance analysis of the university's extracurricular activities in the field of pedagogy. The contents of this study consisted of presenting some facts that should be based on evidence for the advancement of performance analysis of the extracurricular activities after the establishment of the center for extracurricular activities in Duksung Women's University. And in practices, the development and diagnostic analysis of tools for diagnosing extracurricular customized learning capabilities, data-based multidimensional analysis (IR system), continuous monitoring analysis through extracurricular certification, and analysis based on feedback tools were presented in a convergence perspective and context. As a result of the study, the evidence-based practices for the advancement of the performance analysis of the extracurricular activities at Duksung Women's University guarantees the validity and stability of the performance evaluation and feedback system of the extracurricular activities at Duksung Women's University, and has a close influence on the extracurricular education work of other universities analyzed as possible.

• Journal of Appropriate Technology
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• v.5 no.2
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• pp.62-69
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• 2019

Supplying stable electric power is one of the important objectives of the Official Development Assistance (ODA) because it is strongly related with improving living standards and income levels in the region. However, rural areas in many developing countries are not properly connected to national power grids, and even areas connected to national power grids are frequently blacked out due to limitations in power capacity. These lack of electric power is a major obstacle to improving income levels and improving living standards in those areas. The Korea-Tanzania Innovative and Energy Center (iTEC), which was established by Seoul National University in cooperation with the Nelson-Mandela Africa Institute of Science and Technology and Technology (NM-AIST) try to build a small solar power plant using a smart-off grid in rural area of Tanzania, where there is no electricity. 10 kW and 7 kW solar power plants are built in Mkalama Village in Kilimanajaro Region and Ngurdoto Village in Arusha Region to provide power to about 50 households each. In addition, smart monitoring systems were installed to collect data about status of power system and power consumption of each house. iTEC seeks for sustainable improvement the income level and quality of life of rural residents in developing countries through the construction of small solar power plants using smart-off grid, and the implementation of various connected projects.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.33 no.5
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• pp.303-310
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• 2020

In this study, a method for selection of the location and number of monitoring sensors in a building structure using artificial neural networks is proposed. The acceleration-history values obtained from the installed accelerometers are defined as the input values, and the mass and stiffness values of each story in a building structure are defined as the output values. To select the installation location and number of accelerometers, several installation scenarios are assumed, artificial neural networks are obtained, and the prediction performance is compared. The installation location and number of sensors are selected based on the prediction accuracy obtained in this study. The proposed method is verified by applying it to 6- and 10-story structure examples.

• 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.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.288-290
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• 2022

As part of the SOC digitization for smart water management and flood prevention, the government reported that automatic and remote control system for drainage facilities (180 billion won) to 57% of national rivers and established a real-time monitoring system (30 billion won). In addition, they were also planning to establish a smart dam safety management system (15 billion won) based on big data at 11 regions. Therefore, research is needed for smart water management and flood prevention system that can accurately calculate the flow rate through real-time flow rate measurement of rivers. In particular, the most important thing to improve the system implementation and accuracy is to ensure the accuracy of real-time flow rate measurements. To this end, radar sensors for measuring the flow rate of electromagnetic waves in the United States and Europe have been introduced and applied to the system in Korea, but demand for improvement of the system continues due to high price range and performance. Consequently, we would like to propose an improved flow rate measurement and flood forecast system by developing a radar sensor for measuring the electromagnetic surface current meter for real-time flow rate measurement.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.32 no.3
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• pp.213-220
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• 2014

This is the study to conduct the topographical analysis using the orthophotographic data from the waypoint flight using the UAV and constructed the system required for the automatic waypoint flight using the multicopter.. The results of the waypoint photographing are as follows. First, result of the waypoint flight over the area of 9.3ha, take time photogrammetry took 40 minutes in total. The multicopter have maintained the certain flight altitude and a constant speed that the accurate photographing was conducted over the waypoint determined by the ground station. Then, the effect of the photogrammetry was checked. Second, attached a digital camera to the multicopter which is lightweight and low in cost compared to the general photogrammetric unmanned airplane and then used it to check its mobility and economy. In addition, the matching of the photo data, and production of DEM and DXF files made it possible to analyze the topography. Third, produced the high resolution orthophoto(2cm) for the inside of the river and found out that the analysis is possible for the changes in vegetation and topography around the river. Fourth, It would be used for the more in-depth research on landscape analysis such as terrain analysis and visibility analysis. This method may be widely used to analyze the various terrains in cities and rivers. It can also be used for the landscape control such as cultural remains and tourist sites as well as the control of the cultural and historical resources such as the visibility analysis for the construction of DSM.

• Journal of the Earthquake Engineering Society of Korea
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• v.10 no.6 s.52
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• pp.137-145
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• 2006

Recently, the interests in structural monitoring of civil infrastructures are increased. Especially, as the civil infrastructures such as bridges, tunnels and buildings become large-scale, it is necessary to monitor and maintain the safety state of the structures, which requires smart systems that can supply long-term monitoring during the service time of the structures. In this paper, we investigated the possibilities of fiber optic sensor application to the various structures. We investigate the possibility of using fiber optic Bragg grating sensors to joint structure. The sensors show good response to the structural behavior of the joint while electric gauges lack of sensitivity, durability and long term stability for continuous monitoring. We also apply fiber optic structural monitoring to the composite repaired concrete beam structure. Peel-out effects is detected with optical fiber Bragg grating sensors and the strain difference between main structure and repaired carbon sheets is observed when they separate each other. The real field test was performed to verify the behaviors of fiber Bragg grating sensors attached to the containment structure in Uljin nuclear power plant in Korea as a part of structural integrity test which demonstrates that the structural response of the non-prototype primary containment structures. The optical fiber Bragg grating sensor smart system which is the probable means for long term assessments can be applicable to monitoring of structural members in various civil infrastructures.

• Journal of the Korean Institute of Gas
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• v.1 no.1
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• pp.27-32
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• 1997

Continual monitoring of abnormal operating conditions i a key issue in maintaining high product quality and safe operation, since the undetected process abnormality may lead to the undesirable operations, finally producing low quality products, or breakdown of equipment. The statistical projection method recently highlighted has the advantage of easily building reference model with the historical measurement data in the statistically in-control state and not requiring any detailed mathematical model or knowledge-base of process. As the complexity of process increases, however, we have more measurement variables and recycle streams. This situation may not only result in the frequent occurrence of process Perturbation, but make it difficult to pinpoint trouble-making causes or at most assignable source unit due to the confusing candidates. Consequently, an ad hoc skill to monitor and diagnose in plat-wide scale is needed. In this paper, we propose a hierarchical plant-wide monitoring methodology based on hierarchical decomposition and principal component analysis for handling the complexity and interactions among process units. This have the effect of preventing special events in a specific sub-block from propagating to other sub-blocks or at least delaying the transfer of undesired state, and so make it possible to quickly detect and diagnose the process malfunctions. To prove the performance of the proposed methodology, we simulate the Tennessee Eastman benchmark process which is operated continuously with 41 measurement variables of five major units. Simulation results have shown that the proposed methodology offers a fast and reliable monitoring and diagnosis for a large scale chemical plant.