• Korean Journal of Construction Engineering and Management
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• v.14 no.2
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• pp.141-149
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• 2013

This paper presents a scheduling technique that reflects various constraints in remodeling project of inhabited condition. The remodeling project of inhabited condition is required more detailed planning and control due to claims by noise, vibration, dust, smells, limited lift capacity, and limited temporary stock area. Because of the constraints, complexity in scheduling is increased and earlier completion is required to reduce the possibility of safety and environment accidents. Especially, in case of inhabited condition, the scheduling should be linked day-time/night-time/weekend work. This paper proposes a structured scheduling technique to incorporate those constraints in remodeling of inhabited condition. This scheduling technique considers not only remodeling process but also dismantling, newly-construction, and residents movement. Process expression method using MS-Project also presented to keep connectivity with existing scheduling system.

• Structural Engineering and Mechanics
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• v.62 no.2
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• pp.197-208
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• 2017

Among various retrofitting strategies, use of semi-active control for retrofitting a building structure has gained momentum in recent years. One of the techniques for such retrofitting is to connect a weaker building to an adjacent stronger building by semi-active devices, so that performances of a weaker building are significantly improved for seismic forces. In this paper, a ten storey weaker building is connected to an adjacent stronger building using magneto-rheological (MR) dampers, for primarily improving the performance of the weaker building in terms of displacement, drift and base shear. For this, a fuzzy logic controller is specifically developed by fuzzyfying the responses of the coupled system. The performance of the control strategy is compared with the passive-on and passive-off controls. Pounding Mitigation between the two buildings is also investigated using all three control strategies. The results show that there exists a fundamental frequency ratio between the two buildings for which maximum control of the weaker building response takes place with no penalty on the stronger building. There exists also a fundamental frequency ratio where control of the weaker building response is achieved at the expense of the amplification of the stronger building. However, coupling strategy always improves the possibility of pounding mitigation.

• Journal of Convergence for Information Technology
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• v.10 no.8
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• pp.47-52
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• 2020

In this study, a simulation investigation was conducted on the method of reducing switching noise and spurious emission among design methods for step-down DC-DC converter modules for automotive. A typical 4-layer converter circuit using a PMIC(Power Management Integrated Circuit) chip was presented, and the simulation results of conductive emissions at two input terminals (+, -) and the point between the input filter and the PMIC was performed in the 1.0~5.0MHz band and the 100MHz band. The results for the conducted and radiated emissions in the HF(3~30MHz) and VHF(30-300MHz) bands were presented. It showed an improvement of about 10dB over the bands by routing the output terminal placed on the 3 or 4-layer in the opposite direction to the input terminal. The result of this study is expected to be useful in the design of the DC-DC converter modules in the future because it gives a better improvement compared to the existing methods.

• The Journal of Bigdata
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• v.5 no.1
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• pp.227-239
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• 2020

This paper introduces a method for predicting the maximum power demand despite communication errors in industrial sites. Due to the recent policy of de-nuclearization in Korea, the price of electricity is inevitable, and the amount of electricity used and maximum load management for the management of power demand are becoming important issues. Accordingly, it is important to predict and manage peak power. However, problems such as loss and modulation of measured power data occur at industrial sites due to noise generated by various facilities and sensors. It is difficult to predict the exact value when measured effective power data are lost. The study presents a model for predicting and correcting anomalies and missing values when measured effective power data are lost. The models used in this study are expected to be useful in predicting peak power demand in the event of communication errors at industrial sites.

• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.223-232
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• 2014

Life-cycle assessment (LCA) is often employed to quantify the environmental impact of a product in a comprehensive manner. The aspects of performance and usability as well as that of eco-friendliness should be considered in an integrated manner for the market competitiveness of an eco-friendly product. The present study developed a product improvement plan for an eco-friendly electric heater by benchmarking two small-size electric heaters (companies 'H' and 'T') in terms of performance, usability, and eco-friendliness. The performance measurements such as temperature, humidity, wind speed, noise, and power consumption were collected while the two heaters were operated in a laboratory setting. Then, the usability evaluations such as aesthetics, operation satisfaction, performance satisfaction, and overall satisfaction were surveyed for the two heaters using a 5-point scale (1 for very unsatisfied and 5 for very satisfied). Lastly, the LCA analysis was conducted by following the six-step process of eco-friendly product design provided by KEITI. The analysis results of the two products being integrated with the aspects of product, service, and user, four design improvement directions such as eco-efficient, smart, modularized, and user-support were recommended for an eco-friendly electric heater. These proposed concepts would be useful to develop an eco-friendly electric heater design with a high level of market competitiveness.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.18 no.11
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• pp.2745-2752
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• 2014

In recent years, Transport Demand Management has been conducted for the efficient management of transport. In ITS applications in particular, the prerequisite is accurate and reliable positioning. However, the major problems are satellite signal outage, and multipath. This paper proposes that outage and multipath measurement can be detected and estimated using elevation angle and signal to noise ratio data association relation in stand-alone GPS. In order to verify the performance of the proposed method, it is then evaluated by the car test. the evaluation test environment has low accuracy and unreliable positioning because of signal outage or multipath such as steep hill and high buildings. In the evaluation test result, 918times abnormal signal occurred and it was confirmed that the proposed method showed more improved 9.48m(RMS) horizontal positioning error than without proposed method.

• Smart Structures and Systems
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• v.24 no.5
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• pp.567-585
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• 2019

In the past two decades, structural health monitoring (SHM) systems have been widely installed on various civil infrastructures for the tracking of the state of their structural health and the detection of structural damage or abnormality, through long-term monitoring of environmental conditions as well as structural loadings and responses. In an SHM system, there are plenty of sensors to acquire a huge number of monitoring data, which can factually reflect the in-service condition of the target structure. In order to bridge the gap between SHM and structural maintenance and management (SMM), it is necessary to employ advanced data processing methods to convert the original multi-source heterogeneous field monitoring data into different types of specific physical indicators in order to make effective decisions regarding inspection, maintenance and management. Conventional approaches to data analysis are confronted with challenges from environmental noise, the volume of measurement data, the complexity of computation, etc., and they severely constrain the pervasive application of SHM technology. In recent years, with the rapid progress of computing hardware and image acquisition equipment, the deep learning-based data processing approach offers a new channel for excavating the massive data from an SHM system, towards autonomous, accurate and robust processing of the monitoring data. Many researchers from the SHM community have made efforts to explore the applications of deep learning-based approaches for structural damage detection and structural condition assessment. This paper gives a review on the deep learning-based SHM of civil infrastructures with the main content, including a brief summary of the history of the development of deep learning, the applications of deep learning-based data processing approaches in the SHM of many kinds of civil infrastructures, and the key challenges and future trends of the strategy of deep learning-based SHM.

• Health Policy and Management
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• v.31 no.3
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• pp.255-260
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• 2021

Background: Korea National Health Checkup Programs are aimed at the prevention and early detection of cardiovascular disease in adults. To establish a countermeasure for this tendency, The current Korea National Health Checkup Programs have been providing Health Risk Appraisal (HRA) since 2009, thereby focusing on individual lifestyle correction. However, from 2018, the dyslipidemia screening exam cycle has been changed from 2 to 4 years. Methods: In this study, we try to investigate whether policy decisions are valid based on domestic reports that have influenced policy decisions. First, considering the epidemiology of the domestic cardiovascular disease, dyslipidemia, and metabolic syndrome, the change of the 4-year cycle is appropriate or not. Second, whether the research method that applies came to make policy decisions appropriate or not. Third, our study also investigates whether the direction of policy decision was suitable for the second comprehensive national examination plan. Results: The data that are used in the previous study were that of 10 years ago and there also was a problem in selecting the data, especially the use of one of the research methods to calculate the signal to noise ratio that was aimed at improving health had some problems. This is a research method that does not match with the aim itself. Conclusion: Changing the screening cycle for dyslipidemia does not match the recent trend of general screening to effectively prevent cardiovascular disease in improving individual lifestyles in the national health checkup plan. Studying the relationship with metabolic syndrome, which can be an intermediate stage of cardiovascular disease, could be a policy direction that is more suitable for the national health examination comprehensive plan.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.1
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• pp.107-120
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• 2019

Reservoir water level data identify the current water storage of the reservoir, and they are utilized as primary data for management and research of agricultural water. For the reservoir storage management, Korea Rural Community Corporation (KRC) installed water level stations at around 1,600 agricultural reservoirs and has been collecting the water level data every 10 minutes. However, various kinds of outliers due to noise and erroneous problems are frequently appearing because of environmental and physical causes. Therefore, it is necessary to detect outlier and improve the quality of reservoir water level data to utilize the water level data in purpose. This study was conducted to detect and classify outlier and normal data using two different models including the threshold model and the artificial neural network (ANN) model. The results were compared to evaluate the performance of the models. The threshold model identifies the outlier by setting the upper/lower bound of water level data and variation data and by setting bandwidth of water level data as a threshold of regarding erroneous water level. The ANN model was trained with prepared training dataset as normal data (T) and outlier (F), and the ANN model operated for identifying the outlier. The models are evaluated with reference data which were collected reservoir water level data in daily by KRC. The outlier detection performance of the threshold model was better than the ANN model, but ANN model showed better detection performance for not classifying normal data as outlier.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.15-21
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• 2010

In the recent years, various researches about the signal processing have been presented to improve the performance of power analysis. Among these signal processing techniques, the research about the signal compression is not enough than a signal alignment and a noise reduction; even though that can reduce considerably the computation time for the power analysis. But, the existing compression method can sometimes reduce the performance of the power analysis because those are the unsophisticated method not considering the characteristic of the signal. In this paper, we propose the new PCA (principal component analysis)-based signal compression method, which can block the loss of the meaningful factor of the original signal as much as possible, considering the characteristic of the signal. Also, we prove the performance of our method by carrying out the experiment.