• Proceedings of the Korean Vacuum Society Conference
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• 2012.02a
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• pp.549-549
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• 2012

Early detection of cancer biomarkers in the blood is of vital importance for reducing the mortality and morbidity in a number of cancers. From this point of view, immunosensors based on nanowire (NW) and carbon nanotube (CNT) field-effect transistors (FETs) that allow the ultra-sensitive, highly specific, and label-free electrical detection of biomarkers received much attention. Nevertheless 1D nano-FET biosensors showed high performance, several challenges remain to be resolved for the uncomplicated, reproducible, low-cost and high-throughput nanofabrication. Recently, two-dimensional (2D) graphene and reduced GO (RGO) nanosheets or films find widespread applications such as clean energy storage and conversion devices, optical detector, field-effect transistors, electromechanical resonators, and chemical & biological sensors. In particular, the graphene- and RGO-FETs devices are very promising for sensing applications because of advantages including large detection area, low noise level in solution, ease of fabrication, and the high sensitivity to ions and biomolecules comparable to 1D nano-FETs. Even though a limited number of biosensor applications including chemical vapor deposition (CVD) grown graphene film for DNA detection, single-layer graphene for protein detection and single-layer graphene or solution-processed RGO film for cell monitoring have been reported, development of facile fabrication methods and full understanding of sensing mechanism are still lacking. Furthermore, there have been no reports on demonstration of ultrasensitive electrical detection of a cancer biomarker using the graphene- or RGO-FET. Here we describe scalable and facile fabrication of reduced graphene oxide FET (RGO-FET) with the capability of label-free, ultrasensitive electrical detection of a cancer biomarker, prostate specific antigen/${\alpha}$ 1-antichymotrypsin (PSA-ACT) complex, in which the ultrathin RGO channel was formed by a uniform self-assembly of two-dimensional RGO nanosheets, and also we will discuss about the immunosensing mechanism.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.32 no.7
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• pp.51-59
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• 2004

An experimental procedure to identify failure modes of impact damage using sensor signals and to analyze their general features is examined. A series of low-velocity impact tests from low energy to damage-induced high energy were performed on the instrumented drop weight impact tester to monitor the stress wave signals due to failure modes such as matrix cracking, delamination, and fiber breakage. The wavelet transform(WT) and Short Time Fourier Transform(STFT) are used to decompose the piezoelectric sensor signals in this study. The extent of the damage in each case was examined by means of a conventional ultrasonic C-scan. The PVDF sensor signals are shown to carry important information regarding the nature of the impact process that can be extracted from the careful signal processing and analysis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.1
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• pp.143-150
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• 2020

To enhance the effectiveness of the FMS (flexible manufacturing system), it is necessary for the manufacturing control system to be upgraded by integrating the cyber and the physical manufacturing systems. Using the CPPS (Cyber-Physical Production System) concept, this study proposes a 4-stage vertical integration and control framework for an aircraft parts manufacturing plant. In the proposed framework, the process controller prepares the operations schedule for processing work orders generated from the APS (advanced planning & scheduling) system. The scheduled operations and the related control commands are assigned to equipments by the dispatcher of the line controller. The line monitor is responsible for monitoring the overall status of the FMS including work orders and equipments. Finally the process monitor uses the simulation model to check the performance of the production plan using real time plant status data. The W-FMCS (Wing rib-Flexible Manufacturing Control & Simulation) are developed to implement the proposed 4-stage CPPS based FMS control architecture. The effectiveness of the proposed control architecture is examined by the real plant's operational data such as utilization and throughput. The performance improvement examined shows the usefulness of the framework in managing the smart factory's operation by providing a practical approach to integrate cyber and physical production systems.

• The Journal of the Korea Contents Association
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• v.18 no.6
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• pp.324-332
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• 2018

Blockchain technology has the potential to revolutionize trust models and business processes in a variety of industries. However, it is considered to be the initial stage of the system that pursues autonomy rather than efficiency, and it is necessary to monitor and inspect the distributed ledger technology from the price and introduction time as compared with the existing relational DB transaction technology. However, domestic and foreign private sectors have already been activated by applying block-chain technology in the national domain, and the block chain is devoid of doubt that it is an exaggerated technology, characterized by the invariance of the record, transparency, and autonomous execution of business rules. It has begun to be utilized in history, identity, certification and auditing in the financial industry as well as various industries. In this paper, we analyze the problems such as security weakness, insufficient regulatory environment, technical consensus and lack of common standard. In addition, the business sense and possibility of the block chain technology is expected to be the innovation of the industrial ecosystem by entering into the reality system from the concept through monitoring the actual introduction performance in the field of copyright, logistics, health care and environment.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.17 no.2
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• pp.211-218
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• 2017

In this study, we implemented IoT Sensor Network using Mobius Platform and implemented four kinds of Z-wave sensors applicable to smart home service to verify its performance. The 12 common service functions (CSF) provided by Mobius enable application services including status monitoring of sensors to be implemented quickly. The standard service procedures and protocols have eliminated the design process of the system and shortened the meeting time for establishing protocol between application software developer, gateway developer and sensor developer, and discussion time for adjustment of opinion. We confirmed that the application service based on the implemented sensor network and the implementation of IoT sensor can shorten the development schedule, and confirmed that most of the products purchased in the market can be accommodated without change. We hope that such speediness and openness will be able to meet the demands of various services and contribute to expanding services and creating new markets.

• Journal of Digital Convergence
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• v.18 no.8
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• pp.231-242
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• 2020

The thesis presents a system that continuously collects the human body's physiological vital information at rest with sensors and ICT information technology and predicts diabetes using the collected information. it shows the artificial neural network machine learning method and essential basic variable values. The study method analyzed the correlation between heart rate measurements of BCG and ECG sensors in 20 DM- and 15 DM+ subjects. Artificial Neural Network (ANN) machine learning program was used to predictability of diabetes. The input variables are time domain information of HRV, heart rate, heart rate variability, respiration rate, stroke volume, minimum blood pressure, highest blood pressure, age, and sex. ANN machine learning prediction accuracy is 99.53%. Thesis needs continuous research such as diabetic prediction model by BMI information, predicting cardiac dysfunction, and sleep disorder analysis model using ANN machine learning.

• The Journal of Society for e-Business Studies
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• v.22 no.2
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• pp.1-18
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• 2017

Recently, identification of the extremely stressed condition of children is an essential skill for real-time recognition of a dangerous situation because incidents of children have been dramatically increased. In this paper, therefore, we present a model based on machine learning techniques for stress status identification of a child by using bio-signals such as voice and heart rate that are major factors for presenting a child's emotion. In addition, a smart band for collecting such bio-signals and a mobile application for monitoring child's stress status are also suggested. Specifically, the proposed method utilizes stress patterns of children that are obtained in advance for the purpose of training stress status identification model. Then, the model is used to predict the current stress status for a child and is designed based on conventional machine learning algorithms. The experiment results conducted by using a real-world dataset showed that the possibility of automated detection of a child's stress status with a satisfactory level of accuracy. Furthermore, the research results are expected to be used for preventing child's dangerous situations.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.05a
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• pp.603-604
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• 2016

Due to the recent popularization and commercialization of smart devices the change in the IoT(Internet of Things) environment continues. Thus a personalized service to the individual characteristics it is required by the user increases. However, existing systems are provided, there is a problem that only generalized service to the user, because the operation is manually carried out according to the rules set by the developer. In this paper, we design a custom operation management system to solve this problem. User to prioritize each of the sensors through the application Accordingly manages the sensor. This allows the user to perform an action is thought to be for the individual characteristics and can expect a more efficient productivity.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.415-420
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• 2013

U-WHS refers to a means of remote health monitoring service to combine fitness with wellbing. U-WHS is a system which can measure and manage biometric information of patients without any limitation on time and space. In this paper, we performed in order to look into the influence that the encryption module influences on the communication evaluation in the biometric information transmission gone to the smart mobile device and Hospital Information System.In the case of the U-WHS model, the client used the Objective-c programming language for software development of iOS Xcode environment and SEED and HIGHT encryption module was applied. In the case of HIS, the MySQL which is the Websocket API of the HTML5 and relational database management system for the client and inter-server communication was applied. Therefore, in WIFI communication environment, by using wireshark, data transfer rate of the biometric information, delay and loss rate was checked for the evaluation.

• Smart Structures and Systems
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• v.17 no.2
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• pp.209-230
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• 2016

The Canton Tower is a high-rise slender structure with a height of 610 m. A structural health monitoring system has been instrumented on the structure, by which data is continuously monitored. This paper presents an investigation on the identified modal properties of the Canton Tower using ambient vibration data collected during a whole day (24 hours). A recently developed Fast Bayesian FFT method is utilized for operational modal analysis on the basis of the measured acceleration data. The approach views modal identification as an inference problem where probability is used as a measure for the relative plausibility of outcomes given a model of the structure and measured data. Focusing on the first several modes, the modal properties of this supertall slender structure are identified on non-overlapping time windows during the whole day under normal wind speed. With the identified modal parameters and the associated posterior uncertainty, the distribution of the modal parameters in the future is predicted and assessed. By defining the modal root-mean-square value in terms of the power spectral density of modal force identified, the identified natural frequencies and damping ratios versus the vibration amplitude are investigated with the associated posterior uncertainty considered. Meanwhile, the correlations between modal parameters and temperature, modal parameters and wind speed are studied. For comparison purpose, the frequency domain decomposition (FDD) method is also utilized to identify the modal parameters. The identified results obtained by the Bayesian method, the FDD method and a finite element model are compared and discussed.