• Journal of the Korean Institute of Gas
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• v.12 no.2
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• pp.38-41
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• 2008

In this research, we propose a new method for tracking the release rate using the concentration data obtained from the sensor. We used a sensor network that has already been set surrounding the area where hazardous gas releases can occur. From the real-time sensor data, we detected and analyzed releases of harmful materials and their concentrations. Based on the results, the release rate is estimated using the neural network. This model consists of 14 input variables (sensor data, material properties, process information, meteorological conditions) and one output (release rate). The dispersion model then performs the simulation of the expected dispersion consequence by combining the sensor data, GIS data and the diagnostic result of the source term. The result of this study will improve the safety-concerns of residents living next to storage facilities containing hazardous materials by providing the enhanced emergency response plan and monitoring system for toxic gas releases.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.1250-1251
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• 2022

Measuring management is an important part of preventing the collapse of retaining walls in advance by evaluating their stability with a variety of measuring instruments. The current work of measuring management requires considerable human and material resources since measurement companies need to install measuring instruments at various places on the retaining wall and visit the construction site to collect measurement data and evaluate the stability of the retaining wall. It was investigated that the applicability of the current work of measuring management is poor at small and medium-sized urban construction sites(excavation depth<10m) where measuring management is not essential. Therefore, the purpose of this study is to develop a laser sensor-based hardware to support the wall displacement measurements and their control software applicable to small and medium-sized urban construction sites. The 2D lidar sensor, which is more economical than a 3D laser scanner, is applied as element technology. Additionally, the hardware is mounted on the corner strut of the retaining wall, and it collects point cloud data of the retaining wall by rotating the 2D lidar sensor 360° through a servo motor. Point cloud data collected from the hardware can be transmitted through Wi-Fi to a displacement analysis device (notebook). The hardware control software is designed to control the 2D lidar sensor and servo motor in the displacement analysis device by remote access. The process of analyzing the displacement of a retaining wall using the developed hardware and software is as follows: the construction site manager uses the displacement analysis device to 1)collect the initial point cloud data, and after a certain period 2)comparative point cloud data is collected, and 3)the distance between the initial point and comparison point cloud data is calculated in order. As a result of performing an indoor experiment, the analyses show that a displacement of approximately 15 mm can be identified. In the future, the integrated system of the hardware designed here, and the displacement analysis software to be developed can be applied to small and medium-sized urban construction sites through several field experiments. Therefore, effective management of the displacement of the retaining wall is possible in comparison with the current measuring management work in terms of ease of installation, dismantlement, displacement measurement, and economic feasibility.

• Journal of Internet of Things and Convergence
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• v.6 no.3
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• pp.75-80
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• 2020

Recently, the size of the IoT sensor has been decreased and the collecting direction of the IoT sensor for acquiring the data have been changed from 2D to 3D. It makes sensor structure complex. In the fabrication of the complex structure, 3D printing technology has more useful than traditional manufacturing technologies. Among 3D printing technologies, FDM (fused deposition modeling) is a candidate technology to fabricate a small IoT sensor because the price of the machine and the material is cheap. In the FDM process, a 3D shape is made by depositing the melted filament. Recently, the patent of FDM technology is expired and cheat machines are developed based on the open-source. In the FDM process, mechanical properties of a fabricated part is affected by a lots of factors such as the kind of material and process parameters. Among them, infill is affecting the mechanical properties and the production lead time as well. In this work, a new method to optimize the FDM process with the consideration of mechanical property and production lead time was proposed. To verify the method, the fabrications were performed with the different infill rates. The results of tensile tests were analyzed to verify the proposed method.

• Journal of the Korea Society of Computer and Information
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• v.20 no.2
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• pp.71-77
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• 2015

This paper suggested dynamic application framework based data collecting and monitoring technique using wireless sensor network. The development of application for wireless measurement node firmware program integrates with various sensors and performs control. Collecting data of the user application is downloaded from the node onboard process wirelessly. In addition, the user application can change the temperature initial value of the nodes, which enables dynamic sampling of the measurement nodes. Therefore, dynamic sampling control of the nodes can reduce the power consumptions of sensors compared to existing wired data monitoring.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.1386-1390
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• 2007

This paper presents the equipment condition monitoring technology for the process or the equipment using statistical techniques. The equipment condition monitoring system consists of an empirical model to estimate the expected sensor values of process variables and a diagnose model to detect the abnormal condition and to identify the root source of the problem. The empirical model is constructed by the analysis of historic data. The diagnose model uses the sequential probability ratio test (SPRT) technique. The monitoring system was tested with real operating data acquired from the Reactor Coolant Pump Seal in the Nuclear Power Plant. It can detect the system degradation or failure at the early stage since it is able to catch the subtle deviation of process variables from normal condition.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.27-31
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• 2021

VI probe, which is one of various in-situ plasma monitoring sensor, is frequently used for in-situ process monitoring in mass production environment. In this paper, we correlated the plasma etch results with VI probe data with the small amount of gas flow rate changes to propose usefulness of the VI probe in real-time process monitoring. Several different sized contact holes were employed for the etch experiment, and the etched profiles were measured by scanning electron microscope (SEM). Although the shape of etched hole did not show satisfactory relationship with VI probe data, the chamber status changed along the incremental/decremental modification of the amount of gas flow was successfully observed in terms of impedance monitoring.

• Journal of the Korean Statistical Society
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• v.30 no.3
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• pp.481-498
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• 2001

Empirical Bayes procedure of nonparametric estiamtion of cumulative hazard rates based on censored data is considered using the beta process priors of Hjort(1990). Beta process priors with unknown parameters are used for cumulative hazard rates. Empirical Bayes estimators are suggested and asymptotic optimality is proved. Our result generalizes that of Susarla and Van Ryzin(1978) in the sensor that (i) the cumulative hazard rate induced by a Dirichlet process is a beta process, (ii) our empirical Bayes estimator does not depend on the censoring distribution while that of Susarla and Van Ryzin(1978) does, (iii) a class of estimators of the hyperprameters is suggested in the prior distribution which is assumed known in advance in Susarla and Van Ryzin(1978). This extension makes the proposed empirical Bayes procedure more applicable to real dta sets.

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.2
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• pp.223-228
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• 2016

People try to maintain optimal sleep activity to a changing attitude 20-30 times while sleeping 7-8 hours a day. In order to provide optimal sleep, we need to develop sensing device for apply effective various sleep data acquired through display device that can provide program and information which process and analyze information printed out from sensor and sensing system. In this paper, we analyze sleep pattern to detect the toss and turn while a person's sleeping and we develop a wellness pillow that can be active on sleep health management based on the analyzed data for sleep patterns.

• Journal of Sensor Science and Technology
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• v.24 no.3
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• pp.169-174
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• 2015

In this study, we developed a system to detect the various defects in the metallic objects using the phenomenon that the defects cause the changes of the natural resonant frequencies. Our system consists of a FFT Amp, an Auto Impact Hammer, a Hammer controller and a PC. Auto Impact Hammer creates vibrations in the metallic objects when tapped on the surface. These vibrational signals are converted to the voltage signals by an acceleration sensor attached to the metallic part surface. These analog voltage signals were fed into an ADC (analog-digital converter) and an FFT (fast fourier transform) conversion in the FFT Amp to obtain the digital data in the frequency domain. Labview graphical program was used to process the digital data from th FFT amp to display the spectrum. We compared those spectra with the standard spectrum to find the shifts in the resonant frequencies of the metal parts, and thus detecting the defects. We used PCB's acceleration sensor and TI's TMS320F28335 DSP (digital signal processor) to obtain the resolution of 2.93 Hz and to analyze the frequencies up to 44 kHz.

• Journal of Internet Computing and Services
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• v.18 no.6
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• pp.65-73
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• 2017

Congestion occurring at wireless sensor networks(WSNs) causes packet delay and packet drop, which directly affects overall QoS(Quality of Service) parameters of network. Network congestion is critical when important data is to be transmitted through network. Thus, it is significantly important to effectively control the congestion. In this paper, new mechanism to guarantee reliable transmission for the important data is proposed by considering the importance of packet, configuring packet priority and utilizing the settings in routing process. Using this mechanism, network condition can be maintained without congestion in a way of making packet routed through various routes. Additionally, congestion control using packet service time, packet inter-arrival time and buffer utilization enables to reduce packet delay and prevent packet drop. Performance for the proposed mechanism was evaluated by simulation. The simulation results indicate that the proposed mechanism results to reduction of packet delay and produces positive influence in terms of packet loss rate and network lifetime. It implies that the proposed mechanism contributes to maintaining the network condition to be efficient.